diff --git a/ocr_test/ReadMe.txt b/ocr_test/ReadMe.txt
new file mode 100644
index 0000000000000000000000000000000000000000..70bd4063dd09b16481e392154fbecc4c6c131e7b
--- /dev/null
+++ b/ocr_test/ReadMe.txt
@@ -0,0 +1,35 @@
+========================================================================
+    控制台应用程序：ocr_test 项目概述
+========================================================================
+
+应用程序向导已为您创建了此 ocr_test 应用程序。
+
+本文件概要介绍组成 ocr_test 应用程序的每个文件的内容。
+
+
+ocr_test.vcxproj
+    这是使用应用程序向导生成的 VC++ 项目的主项目文件，其中包含生成该文件的 Visual C++ 的版本信息，以及有关使用应用程序向导选择的平台、配置和项目功能的信息。
+
+ocr_test.vcxproj.filters
+    这是使用“应用程序向导”生成的 VC++ 项目筛选器文件。它包含有关项目文件与筛选器之间的关联信息。在 IDE 中，通过这种关联，在特定节点下以分组形式显示具有相似扩展名的文件。例如，“.cpp”文件与“源文件”筛选器关联。
+
+ocr_test.cpp
+    这是主应用程序源文件。
+
+/////////////////////////////////////////////////////////////////////////////
+其他标准文件:
+
+StdAfx.h, StdAfx.cpp
+    这些文件用于生成名为 ocr_test.pch 的预编译头 (PCH) 文件和名为 StdAfx.obj 的预编译类型文件。
+
+/////////////////////////////////////////////////////////////////////////////
+其他注释:
+
+应用程序向导使用“TODO:”注释来指示应添加或自定义的源代码部分。
+
+/////////////////////////////////////////////////////////////////////////////
+附加库:
+libjpeg.lib
+libpng.lib
+zlib.lib
+///////////////////////////////////////////////////////////////////////
diff --git a/ocr_test/bktree.cpp b/ocr_test/bktree.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8150b8bce143d7d48f9983c189a44eedd47b8150
--- /dev/null
+++ b/ocr_test/bktree.cpp
@@ -0,0 +1,164 @@
+#include <stdlib.h>
+#include <string.h>
+#include <stdio.h>
+#include "bktree.h"
+
+static int write_string(BKTree * bktree, char * string, unsigned char len);
+static BKNode * write_new_record(BKTree * bktree, char * string, unsigned char len);
+
+BKTree * bktree_new(int (* distance)(char *, int, char *, int, int)) {
+    BKTree * bktree = (BKTree *)malloc(sizeof(BKTree));
+
+    bktree->tree_size = BKTREE_TREE_SIZE;
+    bktree->tree = (BKNode *)malloc(bktree->tree_size);
+    bktree->tree_cursor = bktree->tree;
+    
+    bktree->strings_size = BKTREE_STRINGS_SIZE;
+    bktree->strings = (char*)malloc(bktree->strings_size);
+    bktree->strings_cursor = bktree->strings;
+
+    bktree->size = 0;
+
+    bktree->distance = distance;
+
+    return bktree;
+}
+
+void bktree_destroy(BKTree * bktree) {
+    free(bktree->tree);
+    free(bktree->strings);
+    free(bktree);
+}
+
+BKNode * bktree_add(BKTree * bktree, char * string, unsigned char len) {
+    if(len > BKTREE_STRING_MAX || len == 0)
+        return NULL;
+
+    if(bktree->size == 0) {
+        return write_new_record(bktree, string, len);
+    }
+
+    BKNode * node = (BKNode *) bktree->tree;
+    while(node) {
+        char * node_str = BKTREE_GET_STRING(bktree, node->string_offset);
+        int node_str_len = BKTREE_GET_STRING_LEN(bktree, node->string_offset);
+
+        int d = bktree->distance(node_str, node_str_len, string, len, -1);
+
+        if(d == 0)
+            return BKTREE_OK;
+
+        if(node->next[d] > 0) {
+            node = bktree->tree + node->next[d];
+        } else {
+            BKNode * new_node = write_new_record(bktree, string, len);
+            node->next[d] = new_node - bktree->tree;
+            return new_node;
+        }
+    }
+
+    return NULL;
+}
+
+// BKResult * bktree_result_new(BKResult * next, BKNode * node, int distance) {
+//     BKResult * result = (BKResult *)malloc(sizeof(BKResult));
+//     result->next = next;
+//     result->distance = distance;
+//     result->string_offset = node->string_offset;
+// 
+//     return result;
+// }
+
+
+void inner_query(BKTree * bktree, BKNode * node, char * string, unsigned char len, int max, std::vector<BKResult>& res) {
+
+    int d = bktree->distance(BKTREE_GET_STRING(bktree, node->string_offset), BKTREE_GET_STRING_LEN(bktree, node->string_offset), string, len, -1);
+
+    int start = d - max < 1 ? 1 : d - max;
+    int stop = d + max + 1;
+    if(stop >= BKTREE_STRING_MAX)
+        stop = BKTREE_STRING_MAX - 1;
+
+    if(d <= max) {
+       // *result_ptr = bktree_result_new(*result_ptr, node, d);
+		BKResult r;
+		r.distance = d;
+		int len = bktree->strings[node->string_offset];
+		char* start = bktree->strings + node->string_offset + 1;
+		char* end = start + len;
+		r.str = std::string(start,end);
+		res.push_back(r);
+    }
+
+    int i;
+    for(i = start; i <= stop; i++) {
+        if(node->next[i] > 0) {
+            inner_query(bktree, bktree->tree + node->next[i], string, len, max, res);
+        }
+    }
+}
+
+std::vector<BKResult> bktree_query(BKTree * bktree, char * string, unsigned char len, int max) {
+	std::vector<BKResult> res;
+    inner_query(bktree, bktree->tree, string, len, max, res);
+    return res;
+}
+
+void bktree_node_print(BKTree * bktree, BKNode * node) {
+    if(bktree == NULL) {
+        printf("bktree is null\n");
+        return;
+    }
+
+    if(node == NULL) {
+        printf("node is null\n");
+        return;
+    }
+
+    printf("String: %s\n", BKTREE_GET_STRING(bktree, node->string_offset));
+    printf("Offset: %ld\n", node - bktree->tree);
+    int i;
+    for(i = 0; i < BKTREE_STRING_MAX; i++)
+        printf("%d ", node->next[i]);
+
+    printf("\n");
+}
+
+static int write_string(BKTree * bktree, char * string, unsigned char len) {
+    while(bktree->strings_cursor - bktree->strings + len + 2 >= bktree->strings_size) {
+        int cursor_offset = bktree->strings_cursor - bktree->strings;
+
+        char * old_strings = bktree->strings;
+        bktree->strings = (char*)malloc(bktree->strings_size * 2);
+        memcpy(bktree->strings, old_strings, bktree->strings_size);
+        free(old_strings);
+
+        //printf("old ptr: %p\n", old_strings);
+        //printf("new ptr: %p\n", bktree->strings);
+
+        bktree->strings_size *= 2;
+        bktree->strings_cursor = bktree->strings + cursor_offset;
+    }
+
+    int original_offset = bktree->strings_cursor - bktree->strings;
+
+    *(bktree->strings_cursor) = len;
+    memcpy(bktree->strings_cursor + 1, string, len);
+    *(bktree->strings_cursor + len + 1) = '\0';
+    bktree->strings_cursor += len + 2;
+
+    return original_offset;
+}
+
+static BKNode * write_new_record(BKTree * bktree, char * string, unsigned char len) {
+    BKNode * node = bktree->tree_cursor++;
+    node->string_offset = write_string(bktree, string, len);
+    
+    int i;
+    for(i = 0; i < BKTREE_STRING_MAX; i++)
+        node->next[i] = 0;
+    
+    bktree->size++;
+    
+    return node;
+}
\ No newline at end of file
diff --git a/ocr_test/bktree.h b/ocr_test/bktree.h
new file mode 100644
index 0000000000000000000000000000000000000000..1c77e09f0f26dd4d9ada6c937627e856b6a35bbc
--- /dev/null
+++ b/ocr_test/bktree.h
@@ -0,0 +1,49 @@
+#define BKTREE_STRINGS_SIZE 4096
+#define BKTREE_TREE_SIZE 1147483648
+
+#define BKTREE_STRING_MAX 24
+
+#define BKTREE_OK 0
+#define BKTREE_FAIL 1
+
+#define BKTREE_GET_STRING(bktree, string_offset) (bktree->strings + string_offset + 1)
+
+#define BKTREE_GET_STRING_LEN(bktree, string_offset) (*(bktree->strings + string_offset))
+
+#include <string>
+#include <vector>
+
+typedef struct {
+    long string_offset;
+    int next[BKTREE_STRING_MAX];
+} BKNode;
+
+typedef struct {
+    int size;
+
+    BKNode * tree;
+    BKNode * tree_cursor;
+    size_t tree_size;
+
+    char * strings;
+    char * strings_cursor;
+    size_t strings_size;
+
+    // word1, len(word1), word2, len(word2), max
+    int (* distance)(char *, int, char *, int, int);
+} BKTree;
+
+struct BKResult_s {
+    int distance;
+	std::string str;
+    //struct BKResult_s * next;
+};
+typedef struct BKResult_s BKResult;
+
+
+BKTree * bktree_new(int (* distance)(char *, int, char *, int, int));
+void bktree_destroy(BKTree * bktree);
+BKNode * bktree_add(BKTree * bktree, char * string, unsigned char len);
+void bktree_node_print(BKTree * bktree, BKNode * node);
+
+std::vector<BKResult> bktree_query(BKTree * bktree, char * string, unsigned char len, int max);
\ No newline at end of file
diff --git a/ocr_test/common.h b/ocr_test/common.h
new file mode 100644
index 0000000000000000000000000000000000000000..33cc5b64d2c3db3c43e444add84b85e4a7e6c5b6
--- /dev/null
+++ b/ocr_test/common.h
@@ -0,0 +1,1824 @@
+#pragma once
+
+#define  _CRT_SECURE_NO_WARNINGS
+#define OPENCV
+#include <caffe/caffe.hpp>
+
+
+
+
+#include "parser.h"
+
+
+#include"opencv/cv.h"
+#include"opencv/highgui.h"
+#include"opencv/cxcore.h"
+
+
+
+#include"math.h"
+
+#include <stdio.h>
+#include <fstream>
+#include <iostream>
+#include<string>
+#include <vector>
+#include <windows.h>
+#include <process.h>
+#include <direct.h> 
+#include <cmath>
+#include <ctime>
+#include <chrono>
+#include <random>
+#include <memory>
+
+
+using namespace caffe;  // NOLINT(build/namespaces)
+using std::string;
+
+
+
+using namespace cv;
+using namespace std;
+
+
+
+
+#define showSteps  0
+#define showdemo 1
+#define USE_OPENCV
+
+
+
+
+int notfound = 2;
+int readtype = 0;//1Ϊ����2Ϊ�ַ�
+
+CascadeClassifier car_cascade;
+CascadeClassifier char_cascade;
+vector<string> file_name, img_path;
+vector<string> imgfileNames;
+
+
+
+void FastFilter(IplImage *img, double sigma)
+{
+	int filter_size;
+
+	// Reject unreasonable demands
+	if (sigma > 200) sigma = 200;
+
+	// get needed filter size (enforce oddness)
+	filter_size = (int)floor(sigma * 6) / 2;
+	filter_size = filter_size * 2 + 1;
+
+	// If 3 sigma is less than a pixel, why bother (ie sigma < 2/3)
+	if (filter_size < 3) return;
+
+	// Filter, or downsample and recurse
+	/*if (filter_size < 10) {
+
+	#ifdef USE_EXACT_SIGMA
+	FilterGaussian(img, sigma)
+	#else
+	cvSmooth( img, img, CV_GAUSSIAN, filter_size, filter_size );
+	#endif
+
+	}
+	else*/ {
+		if (img->width < 2 || img->height < 2) return;
+
+		IplImage* sub_img = cvCreateImage(cvSize(img->width / 2, img->height / 2), img->depth, img->nChannels);
+
+		cvPyrDown(img, sub_img);
+
+		FastFilter(sub_img, sigma / 2.0);
+
+		cvResize(sub_img, img, CV_INTER_LINEAR);
+
+		cvReleaseImage(&sub_img);
+	}
+
+}
+
+void MultiScaleRetinex(IplImage *img, int scales, double *weights, double *sigmas, int gain, int offset)
+{
+	int i;
+	double weight;
+	IplImage *A, *fA, *fB, *fC;
+
+	// Initialize temp images
+	fA = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
+	fB = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
+	fC = cvCreateImage(cvSize(img->width, img->height), IPL_DEPTH_32F, img->nChannels);
+
+
+	// Compute log image
+	cvConvert(img, fA);
+	cvLog(fA, fB);
+
+	// Normalize according to given weights
+	for (i = 0, weight = 0; i < scales; i++)
+		weight += weights[i];
+
+	if (weight != 1.0) cvScale(fB, fB, weight);
+
+	// Filter at each scale
+	for (i = 0; i < scales; i++) {
+		A = cvCloneImage(img);
+		FastFilter(A, sigmas[i]);
+
+		cvConvert(A, fA);
+		cvLog(fA, fC);
+		cvReleaseImage(&A);
+
+		// Compute weighted difference
+		cvScale(fC, fC, weights[i]);
+		cvSub(fB, fC, fB);
+	}
+
+	// Restore
+	cvConvertScale(fB, img, gain, offset);
+
+	// Release temp images
+	cvReleaseImage(&fA);
+	cvReleaseImage(&fB);
+	cvReleaseImage(&fC);
+}
+
+
+
+
+int init_detect(CascadeClassifier &car_cascade, CascadeClassifier &char_cascade) {
+
+	
+	readtype = 2;
+	if (!char_cascade.load("cascade12.xml"))
+	{
+		cerr << "ERROR: Could not load classifier cascade" << endl;
+		return -1;
+	}
+	
+	readtype = 1;
+	if (!car_cascade.load("cascade_11_plane_card_20160912.xml"))
+	{
+		cerr << "ERROR: Could not load classifier cascade" << endl;
+		return -1;
+	}
+}
+
+void InsertSort(int a[], int count)
+{
+	int i, j, temp;
+	for (i = 1; i<count; i++)
+	{
+		temp = a[i];
+		j = i - 1;
+		while (a[j]>temp && j >= 0)
+		{
+			a[j + 1] = a[j];
+			j--;
+		}
+		if (j != (i - 1))
+			a[j + 1] = temp;
+	}
+}
+
+int SizeOfRect(const CvRect& rect)   //���
+{
+	return rect.height*rect.width;
+}
+
+CvRect IntersectRect(CvRect result_i, CvRect resmax_j) {    //����
+	CvRect rectInter;
+
+
+	rectInter.x = max(result_i.x, resmax_j.x);
+	rectInter.y = max(result_i.y, resmax_j.y);
+
+	int xxx = min((result_i.x + result_i.width), (resmax_j.x + resmax_j.width));
+	int yyy = min((result_i.y + result_i.height), (resmax_j.y + resmax_j.height));
+
+	rectInter.width = xxx - rectInter.x;
+	rectInter.height = yyy - rectInter.y;
+
+	return rectInter;
+}
+
+CvRect UnionRect(CvRect resmax_j, CvRect result_i) {   //����
+	CvRect resmax_jj;
+
+	resmax_jj.x = min(result_i.x, resmax_j.x);
+	resmax_jj.y = min(result_i.y, resmax_j.y);
+
+	int xxx = max((result_i.x + result_i.width), (resmax_j.x + resmax_j.width));
+	int yyy = max((result_i.y + result_i.height), (resmax_j.y + resmax_j.height));
+
+	resmax_jj.width = xxx - resmax_j.x;
+	resmax_jj.height = yyy - resmax_j.y;
+
+	return resmax_jj;
+
+}
+
+vector<CvRect> roichoose(vector<CvRect>detectROI, Mat choose_detect_obj) {
+	/***************������δ����ɸѡ*******************/
+	int image_width = choose_detect_obj.cols;
+	int image_height = choose_detect_obj.rows;
+	int judgeheight = image_height*0.3;
+	vector<vector<CvRect>> b;//�������Ƶȼ���Ĵ洢λ��
+	vector<int> d;//�������Ƽ��洢λ��
+				  //	vector<int> shanchu;//��¼��Ҫɾ���ĵ�
+	for (int i = detectROI.size() - 1; i > -1; i--)
+	{
+		if (detectROI[i].height < judgeheight)
+			//			shanchu.push_back(i);
+			detectROI.erase(detectROI.begin() + i);
+	}
+
+	/***************����****************/
+	if (showSteps) {
+		for (int i = 0; i < detectROI.size(); i++) {
+			printf("x: %d \n", detectROI[i].x);
+		}
+	}
+	for (int i = 0; i < detectROI.size(); i++)//ѭ����iָ����ʼ�㣬j������������
+	{
+		if (detectROI[i].height < judgeheight)
+			continue;
+		for (int j = 10; j < (image_width / 7); j++)//jΪ��һ����Χ�ڵĿ�������
+		{
+			/******����*******/
+
+
+
+			/******����*******/
+
+			int k = 1;//k�����������
+			int startpoint = detectROI[i].x;
+			int startpoint_y = detectROI[i].y;
+			int min_distance = 2 * image_width;       //��¼�������λ�õļ��
+			int bestpoint = detectROI.size() + 1; //��¼��ѵ�
+			vector<CvRect> grouppoint;//ÿһ����ѵ�Ĵ洢λ��
+			grouppoint.push_back(detectROI[i]);
+
+			for (int ii = i + 1; ii < detectROI.size(); ii++)//Ѱ�����������ڵ����ŵ�
+			{
+				if (detectROI[ii].height < judgeheight)
+					continue;
+				int distance_x = abs(j + startpoint - detectROI[ii].x);
+				if (distance_x < 0.5*j)                         //ѡȡxֵ���ʵĵ㣬�Ƚ����������λ�õ�ľ���ѡ��С��
+				{
+					int distance = (distance_x)*(distance_x)+0.7*(detectROI[ii].y - startpoint_y)*(detectROI[ii].y - startpoint_y);//���Ǹ�����x�����꣬��y�IJ�ֵӰ���Ȩ�ؽ���
+					distance = sqrt(distance);
+					if (distance < min_distance)
+					{
+						min_distance = distance;
+						bestpoint = ii;
+
+					}
+				}
+			}
+			/******����*******/
+			//		printf("min_distance=%d", min_distance);
+
+			/*******************************************4-10�޸� �ں����Ŀ�������yֵ��Ԥ��λ��bestpoint_y*************************************/
+			while (bestpoint < (detectROI.size() + 1) && min_distance < 2 * j)
+			{
+				k++;
+				grouppoint.push_back(detectROI[bestpoint]);
+				startpoint = detectROI[bestpoint].x;//������ʼλ��
+				int startpoint_y2 = detectROI[bestpoint].y;//����y2
+				min_distance = image_width;
+				bestpoint = detectROI.size() + 1;
+
+				for (int ii = i + 1; ii < detectROI.size(); ii++)//Ѱ�����������ڵ����ŵ�
+				{
+					int distance_x = abs(j + startpoint - detectROI[ii].x);
+					int best_y = 2 * startpoint_y2 - startpoint_y;//�����е�y����
+					int distance_y = abs(detectROI[ii].y - best_y);
+
+					if (distance_x < 0.5*j)                         //ѡȡxֵ���ʵĵ㣬�Ƚ����������λ�õ�ľ���ѡ��С��
+					{
+						int distance = (distance_x)*(distance_x)+0.7*distance_y*distance_y;//���Ǹ�����x�����꣬��y�IJ�ֵӰ���Ȩ�ؽ���
+						distance = sqrt(distance);
+						if (distance < min_distance)
+						{
+							min_distance = distance;
+							bestpoint = ii;
+
+
+						}
+					}
+				}
+				startpoint_y = startpoint_y2;//����y1
+
+			}
+
+
+
+
+			if (grouppoint.size() > 3)
+			{
+				/*if (showSteps)
+				{
+				for (int testshow = 0; testshow < grouppoint.size(); testshow++){
+				printf("x=%d y=%d width=%d height=%d \n", grouppoint[testshow].x, grouppoint[testshow].y, grouppoint[testshow].width, grouppoint[testshow].height);
+				}
+				getchar();
+				}*/
+
+				b.push_back(grouppoint);
+				d.push_back(j);
+			}
+
+		}
+	}
+
+
+	//������λ�������������������Ƶȼ��򣬽��������ԱȽ�ѡ�����Ž�
+
+	double sum_min = 100000;
+	int bestgroup = 0;
+	if (b.size() > 2)
+	{
+		for (int i = 0; i < b.size(); i++)              //����ÿһ����sumֵ,ѡ��bestgroup
+		{
+			double sum = 0;
+
+			for (int j = 1; j < b[i].size(); j++)
+			{
+				int dis = d[i];
+				sum = sum + (b[i][j].x - b[i][j - 1].x - dis)*(b[i][j].x - b[i][j - 1].x - dis) + 4 * (b[i][j].y - b[i][j - 1].y)*(b[i][j].y - b[i][j - 1].y) + 0.5*(b[i][j].height - b[i][j - 1].height)*(b[i][j].height - b[i][j - 1].height) + 0.5*(b[i][j].width - b[i][j - 1].width)*(b[i][j].width - b[i][j - 1].width);
+			}
+			sum = sum / b[i].size();
+			sum = sum / (1 + pow(b[i].size(), 2));            //����������������
+
+															  //				printf("b[i].size=%d sum=%f \n", b[i].size(),sum);
+			if (sum < sum_min)
+			{
+				sum_min = sum;
+				bestgroup = i;
+			}
+		}
+		//		printf("size=%d distacne=%d sum_min=%f \n", b[bestgroup].size(), d[bestgroup], sum_min);
+		//		return b[bestgroup];
+		/*****************��bestgroup�Ļ������ҵ�ǰ��Ŀ�********/
+		int bestnumber = b[bestgroup].size();
+		int bestx0 = b[bestgroup][0].x;//�������������յ�����
+		int besty0 = b[bestgroup][0].y;
+		int bestx1 = b[bestgroup][bestnumber - 1].x;
+		int besty1 = b[bestgroup][bestnumber - 1].y;
+		int bestheight = 0;
+		int bestw = 0;
+		for (int i = 0; i < bestnumber; i++) {
+			bestheight = bestheight + b[bestgroup][0].height;
+			bestw = bestw + b[bestgroup][0].width;
+		}
+
+		bestheight = bestheight / bestnumber;
+		bestw = bestw / bestnumber;
+		int bestwidth = d[bestgroup];
+		/********************************����ǰ��*****************************/
+		int search_startpoint = bestx0;
+		int search_starty = besty0;
+		int searchpoint = detectROI.size() + 1;
+		int search_min_distance = image_width;
+		int presize = b[bestgroup].size();
+
+		for (int i = 0; i < detectROI.size(); i++)
+		{
+			int distance_x = search_startpoint - bestwidth - detectROI[i].x;
+			int distance_y = abs(search_starty - detectROI[i].y);
+			/***************����****************/
+
+			/*	if (distance_x>-0.8*bestwidth && distance_x < 1.8*bestwidth){
+			printf("bestwidth=%d bestheight=%d bestw=%d \n", bestwidth, bestheight, bestw);
+			printf("distance_x=%d x=%d \n", distance_x, detectROI[i].x);
+			printf("width=%d height=%d \n", detectROI[i].width, detectROI[i].height);
+			}*/
+
+
+			/***************����****************/
+			if (distance_x>-0.6*bestwidth && distance_x < 1.8*bestwidth && detectROI[i].height > 0.7*bestheight &&  detectROI[i].height<1.4*bestheight &&   detectROI[i].width  > 0.7*bestw &&  detectROI[i].width < 1.4*bestw && distance_y<0.4*bestheight)  //ѡȡxֵ���ʵĵ㣬�Ƚ����������λ�õ�ľ���ѡ��С�ģ������߶ȿ��ȵ�ɸѡ
+			{
+				int distance = (distance_x)*(distance_x)+0.7*(besty0 - detectROI[i].y)*(besty0 - detectROI[i].y);//���Ǹ�����x�����꣬��y�IJ�ֵӰ���Ȩ�ؽ���
+				distance = sqrt(distance);
+				if (distance < search_min_distance)
+				{
+					search_min_distance = distance;
+					searchpoint = i;
+
+				}
+			}
+		}
+
+
+		while (searchpoint<(detectROI.size() + 1) && search_min_distance<2 * bestwidth)
+		{
+
+			b[bestgroup].insert(b[bestgroup].begin(), detectROI[searchpoint]);
+			search_startpoint = detectROI[searchpoint].x;//������ʼλ��
+			search_starty = detectROI[searchpoint].y;
+			search_min_distance = image_width;
+			searchpoint = detectROI.size() + 1;
+
+			for (int i = 0; i < detectROI.size(); i++)//Ѱ�����������ڵ����ŵ�
+			{
+				int distance_x = search_startpoint - bestwidth - detectROI[i].x;
+				int distance_y = abs(search_starty - detectROI[i].y);
+				/***************����****************/
+				/*if (distance_x>-0.8*bestwidth && distance_x < 1.8*bestwidth && detectROI[i].height > 0.7*bestheight &&  detectROI[i].height<1.6*bestheight){
+				printf("bestwidth=%d bestw=%d bestheight=%d  \n", bestwidth, bestw,bestheight);
+				printf("x=%d distance_x=%d width=%d height=%d \n", detectROI[i].x, distance_x, detectROI[i].width, detectROI[i].height);
+				}*/
+
+				/***************����****************/
+				if (distance_x>-0.6*bestwidth && distance_x < 1.8*bestwidth && detectROI[i].height > 0.7*bestheight &&  detectROI[i].height<1.4*bestheight &&   detectROI[i].width  > 0.7*bestw &&  detectROI[i].width < 1.4*bestw && distance_y<0.4*bestheight)                         //ѡȡxֵ���ʵĵ㣬�Ƚ����������λ�õ�ľ���ѡ��С��
+				{
+
+					int distance = (distance_x)*(distance_x)+0.7*(besty0 - detectROI[i].y)*(besty0 - detectROI[i].y);//���Ǹ�����x�����꣬��y�IJ�ֵӰ���Ȩ�ؽ���
+					distance = sqrt(distance);
+					//					    printf("distance=%d \n", distance);
+					if (distance < search_min_distance)
+					{
+						search_min_distance = distance;
+						searchpoint = i;
+						if (showSteps)
+						{
+							if (search_min_distance < 2 * bestwidth)
+								printf("search_min_distance=%d \n", search_min_distance);
+						}
+					}
+				}
+			}
+		}
+
+
+		/********************************���Ӻ��*****************************/
+		search_startpoint = bestx1;
+		search_starty = besty1;
+		searchpoint = detectROI.size() + 1;
+		search_min_distance = image_width;
+
+
+		for (int i = 0; i < detectROI.size(); i++)
+		{
+			int distance_x = detectROI[i].x - search_startpoint - bestwidth;
+			int distance_y = abs(search_starty - detectROI[i].y);
+			/***************����****************/
+
+			/*	if (distance_x>-0.8*bestwidth && distance_x < 1.8*bestwidth){
+			printf("bestwidth=%d bestheight=%d bestw=%d \n", bestwidth, bestheight, bestw);
+			printf("distance_x=%d x=%d \n", distance_x, detectROI[i].x);
+			printf("width=%d height=%d \n", detectROI[i].width, detectROI[i].height);
+			}*/
+
+
+			/***************����****************/
+			if (distance_x>-0.6*bestwidth && distance_x < 1.8*bestwidth && detectROI[i].height > 0.7*bestheight &&  detectROI[i].height<1.4*bestheight &&   detectROI[i].width  > 0.7*bestw &&  detectROI[i].width < 1.4*bestw && distance_y<0.4*bestheight)  //ѡȡxֵ���ʵĵ㣬�Ƚ����������λ�õ�ľ���ѡ��С�ģ������߶ȿ��ȵ�ɸѡ
+			{
+				int distance = (distance_x)*(distance_x)+0.7*(besty1 - detectROI[i].y)*(besty1 - detectROI[i].y);//���Ǹ�����x�����꣬��y�IJ�ֵӰ���Ȩ�ؽ���
+				distance = sqrt(distance);
+				if (distance < search_min_distance)
+				{
+					search_min_distance = distance;
+					searchpoint = i;
+
+				}
+			}
+		}
+
+
+		while (searchpoint<(detectROI.size() + 1) && search_min_distance<2 * bestwidth)
+		{
+
+			b[bestgroup].push_back(detectROI[searchpoint]);
+			search_startpoint = detectROI[searchpoint].x;//������ʼλ��
+			search_starty = detectROI[searchpoint].y;
+			search_min_distance = image_width;
+			searchpoint = detectROI.size() + 1;
+
+			for (int i = 0; i < detectROI.size(); i++)//Ѱ�����������ڵ����ŵ�
+			{
+				int distance_x = detectROI[i].x - search_startpoint - bestwidth;
+				int distance_y = abs(search_starty - detectROI[i].y);
+				/***************����****************/
+				/*if (distance_x < 0.8*bestwidth)
+				printf("x=%d width=%d height=%d ", detectROI[i].x, detectROI[i].width, detectROI[i].height);*/
+				/***************����****************/
+				if (distance_x>-0.6*bestwidth && distance_x < 1.8*bestwidth && detectROI[i].height > 0.7*bestheight &&  detectROI[i].height<1.4*bestheight &&   detectROI[i].width  > 0.7*bestwidth &&  detectROI[i].width < 1.4*bestwidth && distance_y<0.4*bestheight)                         //ѡȡxֵ���ʵĵ㣬�Ƚ����������λ�õ�ľ���ѡ��С��
+				{
+					int distance = (distance_x)*(distance_x)+0.7*(besty1 - detectROI[i].y)*(besty1 - detectROI[i].y);//���Ǹ�����x�����꣬��y�IJ�ֵӰ���Ȩ�ؽ���
+					distance = sqrt(distance);
+					//					printf("distance=%d \n", distance);
+					if (distance < search_min_distance)
+					{
+						search_min_distance = distance;
+						searchpoint = i;
+						if (showSteps)
+						{
+							if (search_min_distance < 0.8*bestwidth)
+								printf("search_min_distance=%d \n", search_min_distance);
+						}
+					}
+				}
+			}
+		}
+		/*****************��bestgroup�Ļ������ҵ�ǰ��Ŀ����********/
+
+		/********************ѡ��bestgroup�д��ڰ�����ϵ�Ŀ�*********************/
+		vector<int> baohan;
+		for (int i = 0; i < b[bestgroup].size(); i++)
+			for (int j = 0; j < b[bestgroup].size(); j++)
+			{
+				CvRect recti = b[bestgroup][i];
+				CvRect rectj = b[bestgroup][j];
+				/***********vector���Ƴ�Ԫ������*************/
+				if (rectj.x >= recti.x && rectj.y >= recti.y && rectj.x + rectj.width < recti.x + recti.width && rectj.y + rectj.height < recti.y + recti.height)
+				{
+					baohan.push_back(j);
+				}
+			}
+		vector<CvRect> group;//ȥ��������ϵ���bestgroup��������
+		for (int i = 0; i < b[bestgroup].size(); i++) {
+			int kkk = 0;//��kkkΪ0��˵������򲻴��ڱ�������ϵ
+			for (int j = 0; j < baohan.size(); j++)
+			{
+				if (i == baohan[j])
+					kkk++;
+			}
+			if (kkk == 0)
+				group.push_back(b[bestgroup][i]);
+		}
+
+
+
+
+
+
+
+		int finalsize = b[bestgroup].size();
+		if (showSteps) {
+			printf("bestx:\n");
+			for (int i = 0; i < b[bestgroup].size(); i++)
+				printf("%d \n", b[bestgroup][i].x);
+			printf("add=%d \n", (finalsize - presize));
+		}
+		//		return b[bestgroup];
+		return group;
+
+	}
+	else
+	{
+		printf(" not found \n");
+		notfound = 0;
+
+		return detectROI;
+	}
+
+}
+
+vector<CvRect> roinormalization(vector<CvRect>chooseROI, Mat normalization_detect_obj)
+{
+	vector<CvRect> normal;
+	int image_width = normalization_detect_obj.cols;
+	int image_height = normalization_detect_obj.rows;
+	int width_average = 0;
+	int height_average = 0;
+	if (chooseROI.size() > 1) {
+		for (int i = 0; i < chooseROI.size(); i++)
+		{
+			width_average += chooseROI[i].width;
+			height_average += chooseROI[i].height;
+		}
+		width_average = width_average / chooseROI.size();
+		height_average = height_average / chooseROI.size();
+		if (width_average * 5>height_average * 3)
+			height_average = width_average * 5 / 3;
+		else
+			width_average = height_average * 3 / 5;
+		for (int i = 0; i < chooseROI.size(); i++)
+		{
+			CvRect roi_normal;
+			roi_normal.x = chooseROI[i].x + 0.5*chooseROI[i].width - 0.5*width_average;
+			if (roi_normal.x < 0)
+				roi_normal.x = 0;
+			roi_normal.y = chooseROI[i].y + 0.5*chooseROI[i].height - 0.5*height_average;
+			if (roi_normal.y < 0)
+				roi_normal.y = 0;
+			roi_normal.width = width_average;
+			if (roi_normal.width + roi_normal.x>image_width)
+				roi_normal.width = image_width - roi_normal.x;
+			roi_normal.height = height_average;
+			if (roi_normal.height + roi_normal.y>image_height)
+				roi_normal.height = image_height - roi_normal.y;
+			normal.push_back(roi_normal);
+		}
+		return normal;
+	}
+	else
+		return chooseROI;
+}
+
+vector<CvRect> roicomplete(vector<CvRect>roinormalization, Mat normalization_detect_obj)
+{
+	int image_width = normalization_detect_obj.cols;
+	int image_height = normalization_detect_obj.rows;
+	if (roinormalization.size() > 1)
+	{
+		int roiwidth = roinormalization[0].width;
+		int roiheight = roinormalization[0].height;
+		vector<CvRect> com;//comΪС����ʱ�Ĵ洢
+		for (int i = 0; i < roinormalization.size(); i++)
+		{
+			com.push_back(roinormalization[i]);
+		}
+
+		if (showSteps)
+			cout << "��β�м䲹��ǰ����⵽���ַ�����Ϊ��" << roinormalization.size() << endl;
+		int sum = 0;
+		int avg_distance_of_chars = 0;
+		if (roinormalization.size() > 2) {
+			int numSum = 0;
+
+			for (int i = 0; i<roinormalization.size() - 1; i++) { //�����һλ�ų���
+				if ((roinormalization[i + 1].x - roinormalization[i].x - roinormalization[i].width) < roiwidth * 0.1) {
+					if (showSteps)
+						cout << "��" << i << "������ǣ�" << (roinormalization[i + 1].x - (roinormalization[i].x + roinormalization[i].width)) << endl;
+					sum += (roinormalization[i + 1].x - (roinormalization[i].x + roinormalization[i].width));
+					numSum++;
+				}
+			}
+			//�������һλ��ǰ���forѭ���Ѿ��������һλ�˲��ÿ����ˡ���������
+			/*if ((roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x - roinormalization[roinormalization.size() - 2].width) < roiwidth * 0.1)  {
+			if (showSteps)
+			cout << "��" << roinormalization.size() - 1 << "������ǣ�" << roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x - roinormalization[roinormalization.size() - 2].width << endl;
+			sum += (roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x - roinormalization[roinormalization.size() - 2].width);
+			numSum++;
+			}*/
+
+			avg_distance_of_chars = cvFloor((double)sum / (double)numSum);
+		}
+		else if (roinormalization.size() == 2) {
+			avg_distance_of_chars = roinormalization[1].x - roinormalization[0].x - roinormalization[0].width;
+		}
+		if (showSteps)
+			cout << "ƽ�����Ϊ��" << avg_distance_of_chars << endl;
+
+
+
+		for (int i = 1; i < roinormalization.size(); i++)//���м�
+		{
+			int distance = roinormalization[i].x - roinormalization[i - 1].x - roiwidth;//distanceΪ��ǰ�Ŀ�����Ͻ���ǰһ��������Ͻ�֮���x����룬���ص���Ϊ����
+			if (showSteps) {
+				cout << "distance " << distance << endl;
+				cout << "******************��" << roinormalization[i].x - 2.5*(roiwidth + avg_distance_of_chars) << " " << roinormalization[0].x << " " << 0.1*roiwidth << endl;
+			}
+			if (((i == 1) || (i == 2)) && (distance > 0.1*roiwidth))
+			{
+				int j = (distance + 0.2*roiwidth) / (0.8 * roiwidth);//��������0.6-1.4ʱ��һλ��1.4-2.2����λ
+				if (showSteps) {
+					cout << roinormalization[i].x - roinormalization[i - 1].x - roiwidth << " " << (0.6 * roiwidth) << " " << j << endl;
+					cout << (roinormalization[i].x - roinormalization[i - 1].x) % cvRound(0.6*(roiwidth + 2 * avg_distance_of_chars)) << " "
+						<< cvRound(0.6*(roiwidth + 2 * avg_distance_of_chars)) << " " << (roiwidth + 2 * avg_distance_of_chars) << endl;
+				}
+				if ((distance  > 0.6*(roiwidth + 1.5 * avg_distance_of_chars)) && (j == 0))//һ��˵��ƽ�����Ϊ-0.2w,��ǰ�ſ����볬��0.3wʱ���Բ�һλ
+				{
+					j++;
+				}
+				if (showSteps) {
+					cout << "add 1:" << j << " CvRect" << endl;
+				}
+
+				for (int n = 0; n < j; n++) {   //����j = 1�����϶�����ĵ������
+
+					CvPoint centerP;
+					centerP.x = roinormalization[i].x - (2 * (j - n) - 1) * (roinormalization[i].x - (roinormalization[i - 1].x + roiwidth)) / (2 * j);
+					centerP.y = (roinormalization[i].y + roinormalization[i - 1].y) / 2;
+
+					CvRect Roi;
+					Roi.x = centerP.x - roiwidth / 2;
+					Roi.y = centerP.y;
+					Roi.width = roiwidth;
+					Roi.height = roiheight;
+
+					com.push_back(Roi);
+				}
+
+			}
+
+			else if ((roinormalization[i].x - 2.7*(roiwidth + avg_distance_of_chars) > roinormalization[0].x) && (distance > 0.01*roiwidth))//���һ����Ƚ�Զ��Ҳ���ǿ������4λ����
+			{
+				//int j = (roinormalization[i].x - roinormalization[i - 1].x - roiwidth - 2 * avg_distance_of_chars) / (0.6 * roiwidth);//��һ���㷨
+				int j = (roinormalization[i].x - roinormalization[i - 1].x - 0.5*roiwidth) / (roiwidth + avg_distance_of_chars);//��һ���㷨
+																																//int j = (distance + 0.6*roiwidth) / (0.9 * roiwidth);
+				if (showSteps) {
+					cout << roinormalization[i].x - roinormalization[i - 1].x - roiwidth - 2 * avg_distance_of_chars << " " << (0.6 * roiwidth) << " " << j << endl;
+					cout << (roinormalization[i].x - roinormalization[i - 1].x) % cvRound(0.8*(roiwidth + 2 * avg_distance_of_chars)) << " "
+						<< cvRound(0.6*(roiwidth + 2 * avg_distance_of_chars)) << " " << (roiwidth + 2 * avg_distance_of_chars) << endl;
+				}
+				if (((roinormalization[i].x - roinormalization[i - 1].x - roiwidth)  > 0.6*(roiwidth + 2 * avg_distance_of_chars)) && (j == 0)) {
+					j++;
+				}
+				if (showSteps) {
+					cout << "add 2:" << j << " CvRect" << endl;
+				}
+
+				for (int n = 0; n < j; n++) {   //����j = 1�����϶�����ĵ������
+
+					CvPoint centerP;
+					centerP.x = roinormalization[i].x - (2 * (j - n) - 1) * (roinormalization[i].x - (roinormalization[i - 1].x + roiwidth)) / (2 * j);
+					centerP.y = (roinormalization[i].y + roinormalization[i - 1].y) / 2;
+
+					CvRect Roi;
+					Roi.x = centerP.x - roiwidth / 2;
+					Roi.y = centerP.y;
+					Roi.width = roiwidth;
+					Roi.height = roiheight;
+
+					com.push_back(Roi);
+				}
+			}
+		}
+		if (com.size() == 6)//��ǰ��
+		{
+			int size = com.size();
+			CvRect front, front2;//front2��frontǰ�棨����б�Ҫ���ӵĻ���
+			CvRect behind, behind2;//behind2��behind���棨����б�Ҫ���ӵĻ���
+			if (roinormalization[1].x - roinormalization[0].x > roiwidth)//ǰ���һ����͵ڶ���������
+			{
+				if (roinormalization[0].x - 0.8*roiwidth > 0)
+					front.x = roinormalization[0].x - 0.8*roiwidth;
+				else
+					front.x = 0;
+				front.y = 2 * roinormalization[0].y - roinormalization[1].y;
+				if (front.y < 0)
+					front.y = 0;
+				if (front.y + roiheight>image_height)
+					front.y = image_height - roiheight;
+				front.width = roiwidth;
+				front.height = roiheight;
+				if (roinormalization[0].x - front.x > 0.6*roiwidth)
+				{
+					com.insert(com.begin(), front);//�������ӵ�һ�������� �۲��Ƿ����ӵڶ���
+
+				}
+			}
+			else //����
+			{
+				int ddd = roinormalization[1].x - roinormalization[0].x;
+				if (ddd > 0.8*roiwidth)
+					ddd = 0.8*roiwidth;
+				if (roinormalization[0].x - ddd > 0)
+					front.x = roinormalization[0].x - ddd;
+				else
+					front.x = 0;
+				front.y = 2 * roinormalization[0].y - roinormalization[1].y;
+				if (front.y < 0)
+					front.y = 0;
+				if (front.y + roiheight>image_height)
+					front.y = image_height - roiheight;
+				front.width = roiwidth;
+				front.height = roiheight;
+				if (roinormalization[0].x - front.x>0.6*ddd)
+					com.insert(com.begin(), front);
+			}
+			if (roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x > roiwidth)//������һ�͵����ڶ��Ŀ������
+			{
+				if (roinormalization[roinormalization.size() - 1].x + 2 * roiwidth < image_width)
+					behind.x = roinormalization[roinormalization.size() - 1].x + roiwidth;
+				else
+					behind.x = image_width - roiwidth;
+				behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+				if (behind.y < 0)
+					behind.y = 0;
+				if (behind.y + roiheight>image_height)
+					behind.y = image_height - roiheight;
+				behind.width = roiwidth;
+				behind.height = roiheight;
+				if (behind.x - roinormalization[roinormalization.size() - 1].x>0.6*roiwidth)
+					com.push_back(behind);
+			}
+			else//������һ�͵����ڶ��Ŀ������
+			{
+				int fff = roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x;//�����Ϊfff
+				if (roinormalization[roinormalization.size() - 1].x + roiwidth + fff < image_width)//δԽ��
+				{
+					behind.x = roinormalization[roinormalization.size() - 1].x + fff;
+					behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+					if (behind.y < 0)
+						behind.y = 0;
+					if (behind.y + roiheight>image_height)
+						behind.y = image_height - roiheight;
+					behind.width = roiwidth;
+					behind.height = roiheight;
+					com.push_back(behind);
+				}
+				else //Խ��
+				{
+					behind.width = roiwidth;
+					behind.height = roiheight;
+					behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+					if (behind.y < 0)
+						behind.y = 0;
+					if (behind.y + roiheight>image_height)
+						behind.y = image_height - roiheight;
+					behind.x = image_width - roiwidth;
+					com.push_back(behind);
+				}
+			}
+
+		}
+		if (com.size() == 5 || com.size() == 4)
+		{
+			int size = com.size();
+			CvRect front, front2;//front2��frontǰ�棨����б�Ҫ���ӵĻ���
+			CvRect behind, behind2;//behind2��behind���棨����б�Ҫ���ӵĻ���
+			if (roinormalization[1].x - roinormalization[0].x > roiwidth)//ǰ���һ����͵ڶ���������
+			{
+				if (roinormalization[0].x - 0.8*roiwidth > 0)
+					front.x = roinormalization[0].x - 0.8*roiwidth;
+				else
+					front.x = 0;
+				front.y = 2 * roinormalization[0].y - roinormalization[1].y;
+				if (front.y < 0)
+					front.y = 0;
+				if (front.y + roiheight>image_height)
+					front.y = image_height - roiheight;
+				front.width = roiwidth;
+				front.height = roiheight;
+				if (roinormalization[0].x - front.x > 0.7*roiwidth)
+				{
+					com.insert(com.begin(), front);//�������ӵ�һ�������� �۲��Ƿ����ӵڶ���
+					front2.x = front.x - 0.8*roiwidth;
+					front2.y = 2 * front.y - roinormalization[0].y;
+					front2.width = roiwidth;
+					front2.height = roiheight;
+					if (front2.x >= 0 && front2.y >= 0 && front2.y + roiheight< image_height)
+						com.insert(com.begin(), front2);
+
+				}
+			}
+			else //����
+			{
+				/*int ddd = roinormalization[1].x - roinormalization[0].x;
+				if (ddd > 0.8*roiwidth)
+				ddd = 0.8*roiwidth;*/
+				if (roinormalization[0].x - 1.1*roiwidth > 0)
+					front.x = roinormalization[0].x - 1.1*roiwidth;
+				else
+					front.x = 0;
+				front.y = 2 * roinormalization[0].y - roinormalization[1].y;
+				if (front.y < 0)
+					front.y = 0;
+				if (front.y + roiheight>image_height)
+					front.y = image_height - roiheight;
+				front.width = roiwidth;
+				front.height = roiheight;
+				if (roinormalization[0].x - front.x > 0.7*roiwidth)
+				{
+					com.insert(com.begin(), front);
+					front2.x = front.x - 0.8*roiwidth;
+					front2.y = 2 * front.y - roinormalization[0].y;
+					front2.width = roiwidth;
+					front2.height = roiheight;
+					if (front2.x >= 0 && front2.y >= 0 && front2.y + roiheight< image_height)
+						com.insert(com.begin(), front2);
+				}
+			}
+			if (roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x > roiwidth)//������һ�͵����ڶ��Ŀ������
+			{
+				if (roinormalization[roinormalization.size() - 1].x + 2 * roiwidth < image_width)
+					behind.x = roinormalization[roinormalization.size() - 1].x + roiwidth;
+				else
+					behind.x = image_width - roiwidth;
+				behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+				if (behind.y < 0)
+					behind.y = 0;
+				if (behind.y + roiheight > image_height)
+					behind.y = image_height - roiheight;
+				behind.width = roiwidth;
+				behind.height = roiheight;
+				if (behind.x - roinormalization[roinormalization.size() - 1].x > 0.95*roiwidth) {
+					com.push_back(behind);
+					behind2.x = behind.x + roiwidth;
+					behind2.y = 2 * behind.y - roinormalization[roinormalization.size() - 1].y;
+					behind2.width = roiwidth;
+					behind2.height = roiheight;
+					if (behind2.x + roiwidth<image_width && behind2.y >= 0 && behind2.y + roiheight < image_height)
+						com.push_back(behind2);
+
+				}
+			}
+			else//������һ�͵����ڶ��Ŀ������
+			{
+				int fff = roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x;//�����Ϊfff
+				if (roinormalization[roinormalization.size() - 1].x + roiwidth + fff < image_width)//δԽ��
+				{
+					behind.x = roinormalization[roinormalization.size() - 1].x + fff;
+					behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+					if (behind.y < 0)
+						behind.y = 0;
+					if (behind.y + roiheight>image_height)
+						behind.y = image_height - roiheight;
+					behind.width = roiwidth;
+					behind.height = roiheight;
+					com.push_back(behind);
+					behind2.x = behind.x + fff;
+					behind2.y = 2 * behind.y - roinormalization[roinormalization.size() - 1].y;
+					behind2.width = roiwidth;
+					behind2.height = roiheight;
+					if (behind2.x + roiwidth<image_width && behind2.y >= 0 && behind2.y + roiheight < image_height)
+						com.push_back(behind2);
+				}
+				else //Խ��
+				{
+					behind.width = roiwidth;
+					behind.height = roiheight;
+					behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+					if (behind.y < 0)
+						behind.y = 0;
+					if (behind.y + roiheight>image_height)
+						behind.y = image_height - roiheight;
+					behind.x = image_width - roiwidth;
+					if (behind.x - roinormalization[roinormalization.size() - 1].x > 0.7*roiwidth)
+						com.push_back(behind);
+				}
+			}
+
+		}
+
+		return com;
+	}
+	else
+		return roinormalization;
+
+
+}
+
+vector<CvRect> roicomplete2(vector<CvRect>roinormalization, Mat normalization_detect_obj)//ʹ����һ���취����ȫ����
+{
+	int image_width = normalization_detect_obj.cols;
+	int image_height = normalization_detect_obj.rows;
+	if (roinormalization.size() > 1)
+	{
+		int roiwidth = roinormalization[0].width;
+		int roiheight = roinormalization[0].height;
+		vector<CvRect> com;//comΪС����ʱ�Ĵ洢
+		for (int i = 0; i < roinormalization.size(); i++)
+		{
+			com.push_back(roinormalization[i]);
+		}
+		for (int i = 1; i < roinormalization.size(); i++)//���м�
+		{
+			int distance = roinormalization[i].x - roinormalization[i - 1].x - roiwidth;
+			if (distance>0.4*roiwidth)
+			{
+				CvRect buchong;
+				buchong.x = (roinormalization[i].x + roinormalization[i - 1].x) / 2;
+				buchong.y = (roinormalization[i].y + roinormalization[i - 1].y) / 2;
+				buchong.width = roiwidth;
+				buchong.height = roiheight;
+				com.push_back(buchong);
+
+			}
+			else if (i > 2 && distance > 0.1*roiwidth)//����ļ�϶����СһЩ
+			{
+				CvRect buchong2;
+				buchong2.x = (roinormalization[i].x + roinormalization[i - 1].x) / 2;
+				buchong2.y = (roinormalization[i].y + roinormalization[i - 1].y) / 2;
+				buchong2.width = roiwidth;
+				buchong2.height = roiheight;
+				com.push_back(buchong2);
+			}
+		}
+		//if (com.size() == 5)
+		//{
+		//	CvRect add1;
+		//	CvRect add2;
+		//	int carplatepoint = -1;//����������жϳ��Ƶ��λ��
+		//	for (int i = 1; i < roinormalization.size(); i++)
+		//	{
+		//		int pointnumber = 0;
+
+		//	}
+
+		//}
+		//if (com.size() == 5 || com.size() == 6)//��ǰ��
+		//{
+		//	CvRect front;
+		//	CvRect behind;
+		//	if (roinormalization[1].x - roinormalization[0].x > roiwidth)
+		//	{
+		//		if (roinormalization[0].x - 0.8*roiwidth > 0)
+		//			front.x = roinormalization[0].x - 0.8*roiwidth;
+		//		else
+		//			front.x = 0;
+		//		front.y = 2 * roinormalization[0].y - roinormalization[1].y;
+		//		if (front.y < 0)
+		//			front.y = 0;
+		//		if (front.y + roiheight>image_height)
+		//			front.y = image_height - roiheight;
+		//		front.width = roiwidth;
+		//		front.height = roiheight;
+		//		if (roinormalization[0].x - front.x>0.7*roiwidth)
+		//			com.insert(com.begin(), front);
+		//	}
+		//	else
+		//	{
+		//		int ddd = roinormalization[1].x - roinormalization[0].x;
+		//		if (ddd > 0.8*roiwidth)
+		//			ddd = 0.8*roiwidth;
+		//		if (roinormalization[0].x - ddd > 0)
+		//			front.x = roinormalization[0].x - ddd;
+		//		else
+		//			front.x = 0;
+		//		front.y = 2 * roinormalization[0].y - roinormalization[1].y;
+		//		if (front.y < 0)
+		//			front.y = 0;
+		//		if (front.y + roiheight>image_height)
+		//			front.y = image_height - roiheight;
+		//		front.width = roiwidth;
+		//		front.height = roiheight;
+		//		if (roinormalization[0].x - front.x>0.8*ddd)
+		//			com.insert(com.begin(), front);
+		//	}
+		//	if (roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x > roiwidth)//������һ�͵����ڶ��Ŀ������
+		//	{
+		//		if (roinormalization[roinormalization.size() - 1].x + 2 * roiwidth < image_width)
+		//			behind.x = roinormalization[roinormalization.size() - 1].x + roiwidth;
+		//		else
+		//			behind.x = image_width - roiwidth;
+		//		behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+		//		if (behind.y < 0)
+		//			behind.y = 0;
+		//		if (behind.y + roiheight>image_height)
+		//			behind.y = image_height - roiheight;
+		//		behind.width = roiwidth;
+		//		behind.height = roiheight;
+		//		if (behind.x - roinormalization[roinormalization.size() - 1].x>0.95*roiwidth)
+		//			com.push_back(behind);
+		//	}
+		//	else//������һ�͵����ڶ��Ŀ������
+		//	{
+		//		int fff = roinormalization[roinormalization.size() - 1].x - roinormalization[roinormalization.size() - 2].x;//�����Ϊfff
+		//		if (roinormalization[roinormalization.size() - 1].x + roiwidth + fff < image_width)//δԽ��
+		//		{
+		//			behind.x = roinormalization[roinormalization.size() - 1].x + fff;
+		//			behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+		//			if (behind.y < 0)
+		//				behind.y = 0;
+		//			if (behind.y + roiheight>image_height)
+		//				behind.y = image_height - roiheight;
+		//			behind.width = roiwidth;
+		//			behind.height = roiheight;
+		//			com.push_back(behind);
+		//		}
+		//		else if (com.size() == 6)//Խ��
+		//		{
+		//			behind.width = roiwidth;
+		//			behind.height = roiheight;
+		//			behind.y = 2 * roinormalization[roinormalization.size() - 1].y - roinormalization[roinormalization.size() - 2].y;
+		//			if (behind.y < 0)
+		//				behind.y = 0;
+		//			if (behind.y + roiheight>image_height)
+		//				behind.y = image_height - roiheight;
+		//			behind.x = image_width - roiwidth;
+		//			com.push_back(behind);
+		//		}
+		//	}
+
+		//}
+		return com;
+	}
+	else
+		return roinormalization;
+
+
+}
+
+vector<CvRect>buchong(vector<CvRect>ROI_choose, Mat normalization_detect_obj)
+{
+	vector<CvRect> qqq;
+	int image_width = normalization_detect_obj.cols;
+	int image_height = normalization_detect_obj.rows;
+	int srcsize = ROI_choose.size();
+	for (int i = 1; i < srcsize; i++) {
+		qqq.push_back(ROI_choose[i]);
+	}
+	for (int i = 1; i < srcsize; i++)
+	{
+		CvRect left;
+		CvRect right;
+		CvRect up;
+		CvRect down;
+		left.x = ROI_choose[i].x - 4;
+		left.y = ROI_choose[i].y;
+		left.width = ROI_choose[i].width;
+		left.height = ROI_choose[i].height;
+		if (left.x>0)
+			qqq.push_back(left);
+		//		printf("x=%d,y=%d", left.x, left.y);
+
+
+		right.x = ROI_choose[i].x + 4;
+		right.y = ROI_choose[i].y;
+		right.width = ROI_choose[i].width;
+		right.height = ROI_choose[i].height;
+		if (right.x + right.width<image_width)
+			qqq.push_back(right);
+
+		up.x = ROI_choose[i].x;
+		up.y = ROI_choose[i].y - 4;
+		up.width = ROI_choose[i].width;
+		up.height = ROI_choose[i].height;
+		if (up.y > 0)
+			qqq.push_back(up);
+
+		down.x = ROI_choose[i].x;
+		down.y = ROI_choose[i].y + 4;
+		down.width = ROI_choose[i].width;
+		down.height = ROI_choose[i].height;
+		if (down.y + down.height < image_height)
+			qqq.push_back(down);
+	}
+	return qqq;
+
+}
+
+vector<CvRect>hanzibuchong(vector<CvRect>ROI_choose, Mat normalization_detect_obj) {
+	vector<CvRect> qqq;
+	int image_width = normalization_detect_obj.cols;
+	int image_height = normalization_detect_obj.rows;
+
+	qqq.push_back(ROI_choose[0]);
+
+	for (int i = 0; i < 1; i++)
+	{
+		CvRect left;
+		CvRect right;
+		CvRect up;
+		CvRect down;
+		left.x = ROI_choose[i].x - 4;
+		left.y = ROI_choose[i].y;
+		left.width = ROI_choose[i].width;
+		left.height = ROI_choose[i].height;
+		if (left.x>0)
+			qqq.push_back(left);
+		//		printf("x=%d,y=%d", left.x, left.y);
+
+
+		right.x = ROI_choose[i].x + 4;
+		right.y = ROI_choose[i].y;
+		right.width = ROI_choose[i].width;
+		right.height = ROI_choose[i].height;
+		if (right.x + right.width<image_width)
+			qqq.push_back(right);
+
+		up.x = ROI_choose[i].x;
+		up.y = ROI_choose[i].y - 4;
+		up.width = ROI_choose[i].width;
+		up.height = ROI_choose[i].height;
+		if (up.y > 0)
+			qqq.push_back(up);
+
+		down.x = ROI_choose[i].x;
+		down.y = ROI_choose[i].y + 4;
+		down.width = ROI_choose[i].width;
+		down.height = ROI_choose[i].height;
+		if (down.y + down.height < image_height)
+			qqq.push_back(down);
+	}
+	return qqq;
+
+}
+
+char* outputplate(int predict) {
+	switch (predict)
+	{
+	case 0:
+		//printf("0");
+		return "0";
+		break;
+	case 1:
+		//printf("1");
+		return "1";
+		break;
+	case 2:
+		//printf("2");
+		return "2";
+		break;
+	case 3:
+		//printf("3");
+		return "3";
+		break;
+	case 4:
+		//printf("4");
+		return "4";
+		break;
+	case 5:
+		//printf("5");
+		return "5";
+		break;
+	case 6:
+		//printf("6");
+		return "6";
+		break;
+	case 7:
+		//printf("7");
+		return "7";
+		break;
+	case 8:
+		//printf("8");
+		return "8";
+		break;
+	case 9:
+		//printf("9");
+		return "9";
+		break;
+	case 10:
+		//printf("A");
+		return "A";
+		break;
+	case 11:
+		//printf("B");
+		return "B";
+		break;
+	case 12:
+		//printf("C");
+		return "C";
+		break;
+	case 13:
+		//printf("D");
+		return "D";
+		break;
+	case 14:
+		//printf("E");
+		return "E";
+		break;
+	case 15:
+		//printf("F");
+		return "F";
+		break;
+	case 16:
+		//printf("G");
+		return "G";
+		break;
+	case 17:
+		//printf("H");
+		return "H";
+		break;
+	case 18:
+		//printf("J");
+		return "J";
+		break;
+	case 19:
+		//printf("K");
+		return "K";
+		break;
+	case 20:
+		//printf("L");
+		return "L";
+		break;
+	case 21:
+		//printf("M");
+		return "M";
+		break;
+	case 22:
+		//printf("N");
+		return "N";
+		break;
+	case 23:
+		//printf("P");
+		return "P";
+		break;
+	case 24:
+		//printf("Q");
+		return "Q";
+		break;
+	case 25:
+		//printf("R");
+		return "R";
+		break;
+	case 26:
+		//printf("S");
+		return "S";
+		break;
+	case 27:
+		//printf("T");
+		return "T";
+		break;
+	case 28:
+		//printf("U");
+		return "U";
+		break;
+	case 29:
+		//printf("V");
+		return "V";
+		break;
+	case 30:
+		//		printf("W");
+		return "W";
+		break;
+	case 31:
+		//		printf("X");
+		return "X";
+		break;
+	case 32:
+		//		printf("Y");
+		return "Y";
+		break;
+	case 33:
+		//		printf("Z");
+		return "Z";
+		break;
+	case 34:
+		//		printf("ѧ");
+		return "ѧ";
+		break;
+	case 35:
+		//		printf("��");
+		return "��";
+		break;
+	case 36:
+		//		printf("��");
+		return "��";
+		break;
+	case -1:
+		//		printf("?");
+		return "?";
+		break;
+
+	}
+	//	printf(" ");
+
+}
+
+char* outputhanzi(int predict) {
+	switch (predict)
+	{
+	case 0:
+		//printf("��");
+		return "��";
+		break;
+	case 1:
+		//printf("��");
+		return "��";
+		break;
+	case 2:
+		//printf("��");
+		return "��";
+		break;
+	case 3:
+		//printf("��");
+		return "��";
+		break;
+	case 4:
+		//printf("��");
+		return "��";
+		break;
+	case 5:
+		//printf("��");
+		return "��";
+		break;
+	case 6:
+		//printf("��");
+		return "��";
+		break;
+	case 7:
+		//printf("��");
+		return "��";
+		break;
+	case 8:
+		//printf("��");
+		return "��";
+		break;
+	case 9:
+		//printf("��");
+		return "��";
+		break;
+	case 10:
+		//printf("��");
+		return "��";
+		break;
+	case 11:
+		//printf("��");
+		return "��";
+		break;
+	case 12:
+		//printf("��");
+		return "��";
+		break;
+	case 13:
+		//printf("��");
+		return "��";
+		break;
+	case 14:
+		//printf("��");
+		return "��";
+		break;
+	case 15:
+		//printf("³");
+		return "³";
+		break;
+	case 16:
+		//printf("��");
+		return "��";
+		break;
+	case 17:
+		//printf("��");
+		return "��";
+		break;
+	case 18:
+		//printf("��");
+		return "��";
+		break;
+	case 19:
+		//printf("��");
+		return "��";
+		break;
+	case 20:
+		//printf("��");
+		return "��";
+		break;
+	case 21:
+		//printf("��");
+		return "��";
+		break;
+	case 22:
+		//printf("��");
+		return "��";
+		break;
+	case 23:
+		//printf("��");
+		return "��";
+		break;
+	case 24:
+		//printf("��");
+		return "��";
+		break;
+	case 25:
+		//printf("��");
+		return "��";
+		break;
+	case 26:
+		//printf("��");
+		return "��";
+		break;
+	case 27:
+		//printf("ԥ");
+		return "ԥ";
+		break;
+	case 28:
+		//printf("��");
+		return "��";
+		break;
+	case 29:
+		//printf("��");
+		return "��";
+		break;
+	case 30:
+		//printf("��");
+		return "��";
+		break;
+	case -1:
+		//printf("?");
+		return "?";
+		break;
+
+	}
+	//printf(" ");
+}
+
+//BOOL sort_by_x(cv::Point2i point1, cv::Point2i point2) {
+//	return (point1.x < point2.x);
+//}
+
+void show_choose_step(cv::Mat src, vector<CvRect> ROI_choose, char* windName) {
+	if (showSteps)
+	{
+		cv::Mat shaixuan_obj;
+		src.copyTo(shaixuan_obj);
+
+		vector<cv::Scalar> color;
+		cv::Scalar magenta = cv::Scalar(255, 0, 255);
+		magenta = cv::Scalar(255, 0, 0);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(0, 255, 0);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(0, 0, 255);// //Draw rectangle around the face				
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(255, 255, 0);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(255, 0, 255);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(0, 0, 0);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(0, 255, 255);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		magenta = cv::Scalar(100, 13, 200);// //Draw rectangle around the face			
+		color.push_back(magenta);
+
+		color.push_back(magenta);
+		color.push_back(magenta);
+		color.push_back(magenta);
+		color.push_back(magenta);
+
+		for (unsigned int j = 0; j < ROI_choose.size(); j++)
+		{
+			const cv::Rect& single_char_roi = ROI_choose[j];
+			printf("x=%d y=%d w=%d h=%d i=%d \n", single_char_roi.x, single_char_roi.y, single_char_roi.width, single_char_roi.height, j);
+			cv::Point tl(single_char_roi.x, single_char_roi.y);//Get top-left and bottom-right corner points
+			cv::Point br = tl + cv::Point(single_char_roi.width, single_char_roi.height);
+			cv::rectangle(shaixuan_obj, tl, br, color[j], 1, 1, 0);
+			//detectROI0.push_back(single_char_roi);
+		}
+
+		namedWindow(windName, 0);
+		imshow(windName, shaixuan_obj);
+		//cvWaitKey();
+	}
+}
+
+void RandomizeIdx(int *idx, int g_cCountTrainingSample)
+{
+	int i, j;
+
+	srand((unsigned)time(0));
+
+	for (i = 0; i<g_cCountTrainingSample; i++)
+	{
+		j = int((double)rand() / (double)RAND_MAX*double(g_cCountTrainingSample - 1));
+
+		int temp = idx[i];
+		idx[i] = idx[j];
+		idx[j] = temp;
+	}
+}
+
+struct result_
+{
+	vector<cv::Point2i> point;
+	int label = 0;
+	cv::Point2i centor = cv::Point2i(0, 0);
+	int avg_width = 0;
+	int avg_height = 0;
+}result_init;
+
+typedef std::pair<string, float> Prediction;
+static bool PairCompare(const std::pair<float, int>& lhs,
+	const std::pair<float, int>& rhs) {
+	return lhs.first > rhs.first;
+}
+
+/* Return the indices of the top N values of vector v. */
+static std::vector<int> Argmax(const std::vector<float>& v, int N) {
+	std::vector<std::pair<float, int> > pairs;
+	for (size_t i = 0; i < v.size(); ++i)
+		pairs.push_back(std::make_pair(v[i], static_cast<int>(i)));
+	std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);
+
+	std::vector<int> result;
+	for (int i = 0; i < N; ++i)
+		result.push_back(pairs[i].second);
+	return result;
+}
+
+BOOL sort_by_centor(result_ result1, result_ result2) {
+	return (result1.centor.x < result2.centor.x);
+}
+/* Return the top N predictions. */
+
+/* Load the mean file in binaryproto format. */
+BOOL sort_by_x2(cv::Point2i point1, cv::Point2i point2) {
+	return (point1.x < point2.x);
+}
+
+bool sort_by_x(CvRect obj1, CvRect obj2)
+{
+	return obj1.x < obj2.x;
+}
+
+vector<string> split(const string &s, const string &seperator) {
+	vector<string> result;
+	typedef string::size_type string_size;
+	string_size i = 0;
+
+	while (i != s.size()) {
+		//�ҵ��ַ������׸������ڷָ�������ĸ��
+		int flag = 0;
+		while (i != s.size() && flag == 0) {
+			flag = 1;
+			for (string_size x = 0; x < seperator.size(); ++x)
+				if (s[i] == seperator[x]) {
+					++i;
+					flag = 0;
+					break;
+				}
+		}
+
+		//�ҵ���һ���ָ������������ָ���֮����ַ���ȡ����
+		flag = 0;
+		string_size j = i;
+		while (j != s.size() && flag == 0) {
+			for (string_size x = 0; x < seperator.size(); ++x)
+				if (s[j] == seperator[x]) {
+					flag = 1;
+					break;
+				}
+			if (flag == 0)
+				++j;
+		}
+		if (i != j) {
+			result.push_back(s.substr(i, j - i));
+			i = j;
+		}
+	}
+	return result;
+}
+
+int outputplate(string predict) {
+	if (predict == "0")	return 0;
+	else if (predict == "1")	return 1;
+	else if (predict == "2")	return 2;
+	else if (predict == "3")	return 3;
+	else if (predict == "4")	return 4;
+	else if (predict == "5")	return 5;
+	else if (predict == "6")	return 6;
+	else if (predict == "7")	return 7;
+	else if (predict == "8")	return 8;
+	else if (predict == "9")	return 9;
+	else if (predict == "A")	return 10;
+	else if (predict == "B")	return 11;
+	else if (predict == "C")	return 12;
+	else if (predict == "D")	return 13;
+	else if (predict == "E")	return 14;
+	else if (predict == "F")	return 15;
+	else if (predict == "G")	return 16;
+	else if (predict == "H")	return 17;
+	else if (predict == "J")	return 18;
+	else if (predict == "K")	return 19;
+	else if (predict == "L")	return 20;
+	else if (predict == "M")	return 21;
+	else if (predict == "N")	return 22;
+	else if (predict == "P")	return 23;
+	else if (predict == "Q")	return 24;
+	else if (predict == "R")	return 25;
+	else if (predict == "S")	return 26;
+	else if (predict == "T")	return 27;
+	else if (predict == "U")	return 28;
+	else if (predict == "V")	return 29;
+	else if (predict == "W")	return 30;
+	else if (predict == "X")	return 31;
+	else if (predict == "Y")	return 32;
+	else if (predict == "Z")	return 33;
+	else if (predict == "ѧ")	return 34;
+	else if (predict == "��")	return 35;
+	else if (predict == "��")	return 36;
+	else return -1;
+}
+
+int outputhanzi(string predict) {
+	if (predict == "��")	return 0;
+	else if (predict == "��")	return 1;
+	else if (predict == "��")	return 2;
+	else if (predict == "��")	return 3;
+	else if (predict == "��")	return 4;
+	else if (predict == "��")	return 5;
+	else if (predict == "��")	return 6;
+	else if (predict == "��")	return 7;
+	else if (predict == "��")	return 8;
+	else if (predict == "��")	return 9;
+	else if (predict == "��")	return 10;
+	else if (predict == "��")	return 11;
+	else if (predict == "��")	return 12;
+	else if (predict == "��")	return 13;
+	else if (predict == "��")	return 14;
+	else if (predict == "³")	return 15;
+	else if (predict == "��")	return 16;
+	else if (predict == "��")	return 17;
+	else if (predict == "��")	return 18;
+	else if (predict == "��")	return 19;
+	else if (predict == "��")	return 20;
+	else if (predict == "��")	return 21;
+	else if (predict == "��")	return 22;
+	else if (predict == "��")	return 23;
+	else if (predict == "��")	return 24;
+	else if (predict == "��")	return 25;
+	else if (predict == "��")	return 26;
+	else if (predict == "ԥ")	return 27;
+	else if (predict == "��")	return 28;
+	else if (predict == "��")	return 29;
+	else if (predict == "��")	return 30;
+	else return -1;
+}
+
+void detectAndDisplay(Mat frame)
+{
+	std::vector<Rect> faces;
+	Mat frame_gray;
+
+	cvtColor(frame, frame_gray, COLOR_BGR2GRAY);
+	equalizeHist(frame_gray, frame_gray);
+
+	//-- Detect faces
+	/*����imageΪ����ĻҶ�ͼ��objectsΪ�õ����������ľ��ο������飬
+	scaleFactorΪÿһ��ͼ��߶��еij߶Ȳ�����Ĭ��ֵΪ1.1��
+	minNeighbors����Ϊÿһ����������Ӧ�ñ������ڽ�������Ĭ��Ϊ3��
+	flags�����µķ�����û���ã���Ŀǰ��haar���������Ǿɰ�ģ�
+	CV_HAAR_DO_CANNY_PRUNING����Canny��Ե��������ų�һЩ��Ե���ٻ��ߺܶ��ͼ������
+	CV_HAAR_SCALE_IMAGE���ǰ�����������⣬
+	CV_HAAR_FIND_BIGGEST_OBJECTֻ����������壬
+	CV_HAAR_DO_ROUGH_SEARCHֻ�����Լ�⣩��Ĭ��Ϊ0.
+	minSize��maxSize�������Ƶõ���Ŀ������ķ�Χ��*/
+
+	car_cascade.detectMultiScale(frame_gray, faces, 1.15, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(20, 20), Size(170, 170));   // 2. ????
+
+
+	for (size_t i = 0; i < faces.size(); i++)
+	{
+		Point center(faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5);
+		ellipse(frame, center, Size(faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar(255, 0, 255), 1, 8, 0);
+	}
+	//-- Show what you got
+	imshow("cascadetest", frame);
+}
+
+class Timer {
+	using Clock = std::chrono::high_resolution_clock;
+public:
+	/*! \brief start or restart timer */
+	inline void Tic() {
+		start_ = Clock::now();
+	}
+	/*! \brief stop timer */
+	inline void Toc() {
+		end_ = Clock::now();
+	}
+	/*! \brief return time in ms */
+	inline double Elasped() {
+		auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_ - start_);
+		return duration.count();
+	}
+
+private:
+	Clock::time_point start_, end_;
+};
+
+//bool rec_char(caffe::Net& net, cv::Mat src, int& predict, double& loss) {
+//	auto input = net.blob_by_name("data");
+//	input->Reshape({ 1, 1, 35, 21 });//{64, 100, 1, 1}
+//	float *data = input->mutable_cpu_data();//ʶ�������
+//	const int n = input->count();
+//	//cv::Mat src = cv::imread("img/char4.jpg", 0);
+//
+//	cv::Mat src2;
+//	src.convertTo(src2, CV_32F);
+//	cv::resize(src2, src2, cv::Size(21, 35));
+//	for (int i = 0; i < n; ++i) {
+//		data[i] = src2.at<float>(i / src2.cols, i%src2.cols) / 256;  /* nd(gen);*///תͼ����
+//	}
+//	// forward
+//	/*Timer timer;
+//	timer.Tic();*/
+//	net.Forward();
+//#ifndef US_CPP
+//	off_netiof();//close net.txt
+//#endif
+//				 //timer.Toc();
+//				 // visualization
+//	auto images = net.blob_by_name("prob");//gconv5 ��������
+//										   /*std::cout << net.blob_by_name("prob")->shape_string() << std::endl;*/
+//	const int num = images->num();
+//	const int channels = images->channels();
+//	const int height = images->height();
+//	const int width = images->width();
+//	const int canvas_len = std::ceil(std::sqrt(num));
+//	for (int i = 0; i < channels; i++) {
+//		if (i == 0) {
+//			loss = images->mutable_cpu_data()[i];//ȡ��Loss
+//			predict = 0;
+//		}
+//		else {
+//			if (images->mutable_cpu_data()[i]>loss) {
+//				loss = images->mutable_cpu_data()[i];
+//				predict = i; //�ó�ʶ����
+//			}
+//		}
+//		/*std::cout << images->mutable_cpu_data()[i] << std::endl;*/
+//	}
+//	return true;
+//}
+
+
+
+
+
diff --git a/ocr_test/config.txt b/ocr_test/config.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99c9b3cd9b3662f87fa217c62cb46f7c628acd3c
--- /dev/null
+++ b/ocr_test/config.txt
@@ -0,0 +1,6 @@
+path = D:\ocr\plate_card_BLSTM\testData\plate_test_pic\chepai
+startNum = 0
+
+path = C:\plate_card_BLSTM\testData\test
+startNum = 0
+
diff --git a/ocr_test/levenshtein.cpp b/ocr_test/levenshtein.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..705306d2a2ae7eecc06d60e97dd9a6ba9cd13090
--- /dev/null
+++ b/ocr_test/levenshtein.cpp
@@ -0,0 +1,155 @@
+#include <stdlib.h>
+#include <string.h>
+#include "levenshtein.h"
+
+static int minimum(int a,int b,int c)
+/*Gets the minimum of three values*/
+{
+  int min=a;
+  if(b<min)
+    min=b;
+  if(c<min)
+    min=c;
+  return min;
+}
+
+int levenshtein_distance(char *s, int n, char*t, int m, int noop)
+/*Compute levenshtein distance between s and t*/
+{
+  //Step 1
+  int k,i,j,cost,*d,distance;
+  if(n!=0&&m!=0)
+  {
+    d=(int*)malloc((sizeof(int))*(m+1)*(n+1));
+    m++;
+    n++;
+    //Step 2	
+    for(k=0;k<n;k++)
+	d[k]=k;
+    for(k=0;k<m;k++)
+      d[k*n]=k;
+    //Step 3 and 4	
+    for(i=1;i<n;i++)
+      for(j=1;j<m;j++)
+	{
+        //Step 5
+        if(s[i-1]==t[j-1])
+          cost=0;
+        else
+          cost=1;
+        //Step 6			 
+        d[j*n+i]=minimum(d[(j-1)*n+i]+1,d[j*n+i-1]+1,d[(j-1)*n+i-1]+cost);
+      }
+    distance=d[n*m-1];
+    free(d);
+    return distance;
+  }
+  else 
+    return -1; //a negative return value means that one or both strings are empty.
+}
+
+int levenshtein(char * s1, int l1, char * s2, int l2, int threshold) {
+  int * prev_row, * curr_row;
+  int col, row;
+  int curr_row_min, result;
+  int offset = 0;
+
+  /* Do the expensive calculation on a subset of the sequences, if possible, by removing the common prefix. */
+
+  while (s1[offset] == s2[offset]) {
+    offset++;
+  }
+
+  /* Do the expensive calculation on a subset of the sequences, if possible, by removing the common postfix. */
+
+  while ((l1-1 > offset) && (l2-1 > offset) && (s1[l1-1] == s2[l2-1])) {
+    l1--;
+    l2--;
+  }
+
+  l1 -= offset;
+  l2 -= offset;
+
+  /* The Levenshtein algorithm itself. */
+
+  /*       s1=              */
+  /*       ERIK             */
+  /*                        */
+  /*      01234             */
+  /* s2=V 11234             */
+  /*    E 21234             */
+  /*    E 32234             */
+  /*    N 43334 <- prev_row */
+  /*    S 54444 <- curr_row */
+  /*    T 65555             */
+  /*    R 76566             */
+  /*    A 87667             */
+
+  /* Allocate memory for both rows */
+
+  prev_row	= (int*)malloc(l1+1);
+  curr_row	= (int*)malloc(l1+1);
+
+  if ((prev_row == NULL) || (curr_row == NULL)) {
+      return -1;
+  }
+
+  /* Initialize the current row. */
+
+  for (col=0; col<=l1; col++) {
+    curr_row[col] = col;
+  }
+
+  for (row=1; row<=l2; row++) {
+    /* Copy the current row to the previous row. */
+
+    memcpy(prev_row, curr_row, sizeof(int)*(l1+1));
+
+    /* Calculate the values of the current row. */
+
+    curr_row[0]		= row;
+    curr_row_min	= row;
+
+    for (col=1; col<=l1; col++) {
+      /* Equal (cost=0) or substitution (cost=1). */
+
+      curr_row[col]	= prev_row[col-1] + ((s1[offset+col-1] == s2[offset+row-1]) ? 0 : 1);
+
+      /* Insertion if it's cheaper than substitution. */
+
+      if (prev_row[col]+1 < curr_row[col]) {
+        curr_row[col] = prev_row[col]+1;
+      }
+
+      /* Deletion if it's cheaper than substitution. */
+
+      if (curr_row[col-1]+1 < curr_row[col]) {
+        curr_row[col] = curr_row[col-1]+1;
+      }
+
+      /* Keep track of the minimum value on this row. */
+
+      if (curr_row[col] < curr_row_min) {
+        curr_row_min	= curr_row[col];
+      }
+    }
+
+    /* Return nil as soon as we exceed the threshold. */
+
+    if (threshold > -1 && curr_row_min >= threshold) {
+      free(prev_row);
+      free(curr_row);
+
+      return -1;
+    }
+  }
+
+  /* The result is the last value on the last row. */
+
+  result = curr_row[l1];
+
+  free(prev_row);
+  free(curr_row);
+
+  return result;
+}
\ No newline at end of file
diff --git a/ocr_test/levenshtein.h b/ocr_test/levenshtein.h
new file mode 100644
index 0000000000000000000000000000000000000000..79a531531eec3439bb5f11fd5a67f60c243ec14f
--- /dev/null
+++ b/ocr_test/levenshtein.h
@@ -0,0 +1,2 @@
+int levenshtein(char * s1, int l1, char * s2, int l2, int threshold);
+int levenshtein_distance(char *s, int n, char*t, int m, int noop);
\ No newline at end of file
diff --git a/ocr_test/ocr_test.cpp b/ocr_test/ocr_test.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..f44da25135196aa5ab56c7a498feb25e12530c3d
--- /dev/null
+++ b/ocr_test/ocr_test.cpp
@@ -0,0 +1,2188 @@
+// ocr_test.cpp : �������̨Ӧ�ó������ڵ㡣
+//
+
+#include "stdafx.h"
+
+#include <map>
+#include <windows.h>
+#include "public.h"
+
+#include "ICNNPredict.h"
+
+
+//#define CPU_ONLY
+
+#ifndef CPU_ONLY
+#ifdef _DEBUG
+#pragma  comment(lib,"libClassificationd.lib")
+#else
+#pragma  comment(lib,"libClassification.lib")
+#endif
+
+#else
+
+#ifdef _DEBUG
+#pragma  comment(lib,"libClassificationCPU-MKLd.lib")
+#else
+#pragma  comment(lib,"libClassificationCPU-MKL.lib")
+#endif
+#endif
+
+#include "bktree.h"
+#include "levenshtein.h"
+
+#include <time.h>
+#include <string>
+#include <vector>
+#include <fstream>
+#include <iostream>
+#include <set>
+using namespace std;
+
+#include <ctcpp.h>
+
+
+int GetUppercaseNum(const string& str)
+{
+	int n = 0;
+	for (size_t i = 0; i < str.size(); i++)
+	{
+		if (str[i] >= 'A' && str[i] <= 'Z')
+			n++;
+	}
+	return n;
+}
+
+
+std::wstring string2wstring(const string& str, bool bSrcIsUTF8 = true)
+{
+#ifdef _WIN32
+	UINT srcCode = bSrcIsUTF8 ? CP_UTF8 : CP_ACP;
+	int len = ::MultiByteToWideChar(srcCode,
+		0,
+		str.c_str(),
+		-1,
+		NULL,
+		0);
+	if (len == 0)
+		return wstring();
+
+	WCHAR* dst = new WCHAR[len];
+	int nRet = ::MultiByteToWideChar(srcCode,
+		0,
+		str.c_str(),
+		-1,
+		dst,
+		len);
+#else
+	//printf("=====str====%s,len=%lu\n", str.c_str(), str.size());
+	wstring wstr = convert_mb2wc("utf-8", "ucs-2", str);
+	// 	if (wstr.size() == 0)
+	// 		wstr = convert_mb2wc("gb2312", "ucs-2", str);
+	// 	if(wstr.size()==0)
+	// 		wstr = convert_mb2wc("ascii", "ucs-2", str);
+
+#endif
+
+	wstring wstr = dst;
+	delete[]dst;
+
+
+	return wstr;
+}
+
+
+
+void FindAllImages(const char *folder, std::vector<std::string>& vImgPaths, bool bSubFolder)
+{
+
+#ifdef WIN32
+	char szPathName[MAX_PATH];
+	strcpy_s(szPathName, folder);
+	if (szPathName[strlen(szPathName) - 1] != '\\')
+		strcat_s(szPathName, "\\");
+
+	char szFileName[256];
+	strcpy_s(szFileName, szPathName);
+	strcat_s(szFileName, "*.*");
+
+	int ret = 0;
+	WIN32_FIND_DATA wfd;
+	HANDLE hFind = FindFirstFile(szFileName, &wfd);
+	if (hFind != INVALID_HANDLE_VALUE)
+	{
+		do
+		{
+			if (strcmp(wfd.cFileName, ".") == 0 || strcmp(wfd.cFileName, "..") == 0)
+				continue;
+
+			if (wfd.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY)
+			{
+				if (bSubFolder)
+				{
+					strcpy_s(szFileName, szPathName);
+					strcat_s(szFileName, wfd.cFileName);
+					FindAllImages(szFileName, vImgPaths, bSubFolder);
+				}
+			}
+			else
+			{
+				if (strlen(wfd.cFileName) >= 5)
+				{
+					char *ext3 = wfd.cFileName + strlen(wfd.cFileName) - 3;
+					char *ext4 = ext3 - 1;
+					if (_stricmp(ext3, "bmp") == 0
+						|| _stricmp(ext3, "jpg") == 0
+						|| _stricmp(ext3, "JPG") == 0
+						|| _stricmp(ext4, "jpeg") == 0
+						|| _stricmp(ext4, "JPEG") == 0
+						|| _stricmp(ext3, "png") == 0
+						|| _stricmp(ext3, "gif") == 0)
+					{
+						//printf("%s\n", wfd.cFileName);
+
+						char filename[256];
+						sprintf_s(filename, "%s%s", szPathName, wfd.cFileName);
+						vImgPaths.push_back(filename);
+					}
+				}
+			}
+		} while (FindNextFile(hFind, &wfd) != 0);
+	}
+#else
+
+	DIR* pDir = NULL;
+	struct dirent* ent = NULL;
+
+	pDir = opendir(folder);
+	if (pDir == 0)
+	{
+		printf("open folder(%s) FAIL\n", folder);
+		return;
+	}
+
+	std::string strFolder = folder;
+	if (strFolder[strFolder.size() - 1] != '/')
+		strFolder += "/";
+
+	while (ent = readdir(pDir))
+	{
+		if (ent->d_type & DT_DIR)
+			continue;
+
+		int len = (int)strlen(ent->d_name);
+		if (len >= 5 && strcmp(ent->d_name + len - 4, ".jpg") == 0)
+		{
+			char filepath[256];
+			sprintf(filepath, "%s%s", strFolder.c_str(), ent->d_name);
+			vImgPaths.push_back(filepath);
+		}
+	}
+
+	closedir(pDir);
+
+#endif
+
+}
+
+
+string int2str(int val)
+{
+	char buf[64] = "";
+	_snprintf(buf, sizeof(buf) - 1, "%d", val);
+	return buf;
+}
+
+int str2int(const string &val)
+{
+	return atoi(val.c_str());
+}
+
+
+string GetPredictString(const vector<float>& fm, int idxBlank, const vector<string>& labels)
+{
+	string str;
+	for (size_t t = 0; t < fm.size(); t++)
+	{
+		int idx = t;
+		int label = (int)fm[idx] + 0.5f;
+		if (label >= 0 && label != idxBlank)
+		{
+			//str += ("*" + labels[label] + "(" + int2str(label) + ")*");
+			str +=  labels[label];
+		}
+	}
+	return str;
+}
+
+
+float GetCTCLoss(float*activations, int timesteps, int alphabet_size, int blank_index_,
+	const string& strlabel, const std::map<wchar_t, int>& mapLabel2Idx)
+{
+	size_t workspace_alloc_bytes_;
+
+	ctcOptions options;
+	options.loc = CTC_CPU;
+	options.num_threads = 8;
+	options.blank_label = blank_index_;
+
+	int len = strlabel.size();
+	ctcStatus_t status = CTC::get_workspace_size<float>(&len,
+		&timesteps,
+		alphabet_size,
+		1,
+		options,
+		&workspace_alloc_bytes_);
+	//CHECK_EQ(status, CTC_STATUS_SUCCESS) << "CTC Error: " << ctcGetStatusString(status);
+	vector<float> workspace_(workspace_alloc_bytes_);
+
+	vector<int> flat_labels;
+	for (size_t i = 0; i < strlabel.size(); i++)
+	{
+		map<wchar_t, int>::const_iterator it = mapLabel2Idx.find(strlabel[i]);
+		if (it != mapLabel2Idx.end())
+			flat_labels.push_back(it->second);
+	}
+	if (flat_labels.size() != strlabel.size())
+		return 0;
+	float cost = 0;
+	status = CTC::compute_ctc_loss_cpu<float>(activations,
+		0,
+		flat_labels.data(),
+		&len,
+		&timesteps,
+		alphabet_size,
+		1,
+		&cost,
+		workspace_.data(),
+		options
+		);
+	return cost;
+}
+
+void test_ocr_english(const string& imgfolder, const string& modelfolder, const string& lexiconfile)
+{
+#ifdef CPU_ONLY
+	bool usegpu = false;
+#else
+	bool usegpu = true;
+#endif
+
+	//load model
+	ICNNPredict* pCNN = CreatePredictInstance(modelfolder.c_str(), usegpu);
+	int wstd = 0, hstd = 0;
+	pCNN->GetInputImageSize(wstd, hstd);
+
+	//get alphabet
+	vector<string> alphabets = pCNN->GetLabels();
+
+	int idxBlank = 0;
+	vector<string>::const_iterator it = find(alphabets.begin(), alphabets.end(), "blank");
+	if (it != alphabets.end())
+		idxBlank = (int)(it - alphabets.begin());
+
+
+	map<wchar_t, int> mapLabel2IDs;
+	for (size_t i = 0; i < alphabets.size(); i++)
+	{
+		wchar_t c = 0;
+		if (alphabets[i] == "blank")
+			continue;
+		wstring wlabel = string2wstring(alphabets[i], true);
+		mapLabel2IDs.insert(make_pair(wlabel[0], i));
+	}
+
+
+	//init BK-tree of lexicon
+	printf("init BK-tree of lexicon\n");
+	BKTree* pBKtree = bktree_new(levenshtein_distance);
+	ifstream fslexicon(lexiconfile);
+
+	int n = 0;
+	int caseoffset = 'A' - 'a';
+	string line;
+
+	while (getline(fslexicon, line))
+	{
+		if (line.size() == 0)
+			continue;
+		//if(line[line.size()-1]=='\t')
+		bktree_add(pBKtree, const_cast<char*>(line.c_str()), line.size());
+		n++;
+		if (GetUppercaseNum(line) == 0)//ȫ����Сд�ģ�ת�ɴ�д�����ӣ�ת������ĸ��д������
+		{
+			line[0] += caseoffset;
+			bktree_add(pBKtree, const_cast<char*>(line.c_str()), line.size());
+			n++;
+			if (line.size() > 1)
+			{
+				for (size_t i = 1; i < line.size(); i++)
+				{
+					if (line[i] >= 'a' && line[i] <= 'z')
+						line[i] += caseoffset;
+				}
+				bktree_add(pBKtree, const_cast<char*>(line.c_str()), line.size());
+				n++;
+			}
+		}
+		printf("\r%d", n);
+	}
+	printf("\n");
+
+
+	int sumspend = 0;
+	int nok_lexicon = 0;
+	int nok_nolexicon = 0;
+
+	vector<string> imgs;
+	FindAllImages(imgfolder.c_str(), imgs, false);
+
+
+
+	for (size_t i=0;i<imgs.size();i++)
+	{
+		string imgfile = imgs[i];
+		cv::Mat img = cv::imread(imgfile, CV_LOAD_IMAGE_COLOR);
+		int w = img.cols, h = img.rows;
+		if (2 * w <= h)
+		{
+			cv::transpose(img, img);
+			cv::flip(img, img, 1);
+			w = img.cols, h = img.rows;
+		}
+
+
+		int w1 = hstd*w / h;
+		if (w1 != w && h != hstd)
+			cv::resize(img, img, cv::Size(w1, hstd));
+
+		int start = clock();
+
+		vector<int> shape;
+		vector<float> pred = pCNN->GetOutputFeatureMap(img, shape);
+
+		int end = clock();
+		sumspend += (end - start);
+
+
+		string strpredict0 = GetPredictString(pred, idxBlank, alphabets);
+
+		printf("[%d/%d]%s\n\torig result: %s\n",i+1,imgs.size(),imgs[i].c_str(), strpredict0.c_str());
+
+		string strpredict = strpredict0;
+
+
+		int dist = std::min(2, (int)strpredict0.size() / 3);
+		vector< BKResult> ress = bktree_query(pBKtree, const_cast<char*>(strpredict0.c_str()), strpredict0.size(), dist);
+
+		float min_ctc_loss = 1000;
+		vector<int> outshape;
+		vector<float> activitas = pCNN->GetLayerFeatureMaps("fc1x", outshape);;
+		int timesteps = outshape[0];
+		int min_ctc_idx = -1;
+		for (size_t j = 0; j < ress.size(); j++)
+		{
+			float ctcloss = GetCTCLoss(activitas.data(), timesteps, alphabets.size(), idxBlank, ress[j].str, mapLabel2IDs);
+#ifdef _DEBUG
+			printf("%s, ctc loss=%f\n", ress[j].str.c_str(), ctcloss);
+#endif
+			if (ctcloss < min_ctc_loss)
+			{
+				min_ctc_loss = ctcloss;
+				min_ctc_idx = (int)j;
+			}
+		}
+
+		if (ress.size()>0 && min_ctc_idx >= 0)
+			printf("\tdic result: %s\n", ress[min_ctc_idx].str.c_str());
+
+		//printf("%d, mean spend=%.2f",	i+1, sumspend / (float)(i+1));
+
+	}
+
+
+	bktree_destroy(pBKtree);
+
+}
+
+
+void test_ocr_chinese(const string& imgfolder, const string& modelfolder)
+{
+#ifdef CPU_ONLY
+	bool usegpu = false;
+#else
+	bool usegpu = true;
+#endif
+
+	//load model
+	ICNNPredict* pCNN = CreatePredictInstance(modelfolder.c_str(), usegpu);
+	int wstd = 0, hstd = 0;
+	pCNN->GetInputImageSize(wstd, hstd);
+
+	//get alphabet
+	vector<string> alphabets = pCNN->GetLabels();
+
+	int idxBlank = 0;
+	vector<string>::const_iterator it = find(alphabets.begin(), alphabets.end(), "blank");
+	if (it != alphabets.end())
+		idxBlank = (int)(it - alphabets.begin());
+
+
+	map<wchar_t, int> mapLabel2IDs;
+	for (size_t i = 0; i < alphabets.size(); i++)
+	{
+		wchar_t c = 0;
+		if (alphabets[i] == "blank")
+			continue;
+		wstring wlabel = string2wstring(alphabets[i], true);
+		mapLabel2IDs.insert(make_pair(wlabel[0], i));
+	}
+
+
+	int sumspend = 0;
+	int nok_lexicon = 0;
+	int nok_nolexicon = 0;
+
+	vector<string> imgs;
+	FindAllImages(imgfolder.c_str(), imgs, false);
+
+	for (size_t i = 0; i < imgs.size(); i++)
+	{
+		string imgfile = imgs[i];
+		cv::Mat img = cv::imread(imgfile, CV_LOAD_IMAGE_COLOR);
+		int w = img.cols, h = img.rows;
+		if (2 * w <= h)
+		{
+			cv::transpose(img, img);
+			cv::flip(img, img, 1);
+			w = img.cols, h = img.rows;
+		}
+
+
+		//int w1 = hstd*w / h;
+		int w1 = 280;
+		if (w1 != w && h != hstd)
+			cv::resize(img, img, cv::Size(w1, hstd));
+
+		int start = clock();
+
+		vector<int> shape;
+		vector<float> pred = pCNN->GetOutputFeatureMap(img, shape);
+
+		int end = clock();
+		sumspend += (end - start);
+
+		string strpredict0 = GetPredictString(pred, idxBlank, alphabets);
+
+		printf("[%d/%d]%s: %s\n", i + 1, imgs.size(), imgs[i].c_str(), strpredict0.c_str());
+
+		cv::namedWindow("img",0);
+		cv::imshow("img", img);
+		cv::waitKey();
+
+	}
+}
+
+
+int rec_test_main()
+{
+#if 0
+	string imgfolder = "I:\\OCR_Line\\synth_english\\db_read_test\\";
+	string modelfolder = "I:\\OCR_Line\\synth_english\\crnn\\crnn_256\\";
+	string lexiconfile = "I:\\OCR_Line\\synth_english\\lexicon.txt";
+
+	test_ocr_english(imgfolder, modelfolder, lexiconfile);
+
+#else
+	//string imgfolder = "F:\\plate_card_rec\\data\\multilabel_fcn\\right_vision1\\";
+	string imgfolder = "C:\\plate_card_BLSTM\\vs2013_caffe_BN_multi_label_kenel_w\\water_meter_caffe_old\\face\\";
+	string modelfolder = ".\\plateCard_test\\";
+	test_ocr_chinese(imgfolder, modelfolder);
+#endif
+}
+
+
+
+///////////////////////////////
+#include <caffe/caffe.hpp>
+#include "boost/make_shared.hpp"
+#include "caffe-gpu.h"
+
+using namespace caffe;
+
+
+
+#include <string>
+#include <vector>
+#include <fstream>
+#include <direct.h>
+#include <iostream>
+#include <stdlib.h>
+#include <stdio.h>
+#include <time.h>
+#include <fstream>
+#include <sstream>
+#include <exception>
+#include <vector>
+#include <io.h>
+
+
+
+#ifndef HAAR_CASCADE
+#include "common.h"
+#include "classifierCaffe.h"
+#include "opencv2/opencv.hpp"
+
+
+typedef std::pair<CvRect, char*> Pred;
+struct result_plate {
+	CvRect coordinate;
+	vector<Pred> one_char;
+};
+
+
+_declspec(dllexport) bool rec_char(Classifier &net, cv::Mat src, int& predict, double& loss) {
+	
+	vector<float> predictions = net.Classify(src);
+
+	/* Print the top N predictions. */
+	/*for (size_t i = 0; i < predictions.size(); ++i) {
+		std::cout << predictions[i] << " ";
+	}
+	std::cout << std::endl << std::endl;*/
+
+	int N = 1;
+	std::vector<int> maxN = Argmax(predictions, N);
+	for (int i = 0; i < N; ++i) {
+		predict = maxN[i];
+		loss = predictions[predict];
+	}
+
+
+	return true;
+}
+
+
+#endif // DEBUG
+
+
+
+#define showSteps 0
+
+using namespace std;
+char * configFile = "config.txt";
+
+
+char* trainSetPosPath = (char *)malloc(200 * sizeof(char));
+char* templateName = (char *)malloc(200 * sizeof(char));
+int frame_width = 640;
+int frame_height = 480;
+char *model_file = (char *)malloc(200 * sizeof(char));		 
+char *trained_file = (char *)malloc(200 * sizeof(char));	 
+int label_file = 256;
+
+void readConfig(char* configFile, char* trainSetPosPath) {
+	fstream f;
+	char cstring[1000];
+	int readS = 0;
+	f.open(configFile, fstream::in);
+	char param1[200]; strcpy(param1, "");
+	char param2[200]; strcpy(param2, "");
+	char param3[200]; strcpy(param3, "");
+
+	//--��ȡ��һ�У�--
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %s", param1, param2, param3);
+	strcpy(trainSetPosPath, param3);
+
+	//--��ȡ��2�У�-- �Ա�����
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %s", param1, param2, param3);
+	strcpy(templateName, param3);
+
+	//--��ȡ��3�У�-- �����
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %d", param1, param2, &frame_width);
+
+	//--��ȡ��4�У�-- �����
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %d", param1, param2, &frame_height);
+
+	//--��ȡ��5�У�-- ѵ��ģ��
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %s", param1, param2, param3);
+	strcpy(model_file, param3);
+
+	//--��ȡ��6�У�-- ѵ��Ȩ��
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %s", param1, param2, param3);
+	strcpy(trained_file, param3);
+
+	//--��ȡ��6�У�-- ��������
+	f.getline(cstring, sizeof(cstring));
+	readS = sscanf(cstring, "%s %s %d", param1, param2, &label_file);
+}
+
+//����config.txt��ĸ�Ŀ¼�µ����е��ļ���������Ŀ¼��--
+// ������Ŀ¼�����־���label����Ŀ¼����ļ�Ϊlabel���ڵ�ѵ����������---
+vector<string> imgNames;
+vector<string> imgLists;
+vector<int> imgLabels;
+int labelTemp = 0;
+
+void dfsFolder(string folderPath) {
+	_finddata_t FileInfo;
+	string strfind = folderPath + "\\*";
+	long long Handle = _findfirst(strfind.c_str(), &FileInfo);
+	if (Handle == -1L)
+	{
+		cerr << "can not match the folder path" << endl;
+		exit(-1);
+	}
+	do {
+		//�ж��Ƿ�����Ŀ¼--
+		if (FileInfo.attrib & _A_SUBDIR) {
+			//	cout<<FileInfo.name<<" "<<FileInfo.attrib<<endl;
+			//���������Ҫ--
+			if ((strcmp(FileInfo.name, ".") != 0) && (strcmp(FileInfo.name, "..") != 0)) {
+				string newPath = folderPath + "\\" + FileInfo.name;
+				cout << FileInfo.name << " " << newPath << endl;
+				//��Ŀ¼���µ���Ŀ¼���־���label�������û����Ŀ¼����Ϊ��Ŀ¼��
+				labelTemp = atoi(FileInfo.name);
+				//	printf("%d\n",labelTemp);
+				dfsFolder(newPath);
+			}
+		}
+		else {
+			string finalName = folderPath + "\\" + FileInfo.name;
+			//�����е��ļ���д��һ��txt�ļ�--
+			//	cout << FileInfo.name << "\t";
+			//	printf("%d\t",label);
+			//	cout << folderPath << "\\" << FileInfo.name  << " " <<endl;
+			//���ļ����ֺ�label���֣���Ŀ¼���ָ�ֵ��������--
+			imgLabels.push_back(labelTemp);
+			imgNames.push_back(finalName);
+
+
+			std::stringstream ss;
+			std::string str;
+			ss << labelTemp;
+			ss >> str;
+
+			string finalList = FileInfo.name;
+			imgLists.push_back(finalList);
+
+		}
+	} while (_findnext(Handle, &FileInfo) == 0);
+	_findclose(Handle);
+
+}
+
+void initTrainImage() {
+	readConfig(configFile, trainSetPosPath);
+
+	string folderPath = trainSetPosPath;
+	//	string folderPath = "H:\\char\\poker_rec_char_equalist_test";
+	dfsFolder(folderPath);
+}
+
+
+////////////////////////////////////////////
+
+
+
+
+int detect_test_main(int argc, char **argv) {
+	initTrainImage();
+
+	/*::google::InitGoogleLogging(argv[0]);*/
+	double threshold[3] = { 0.7, 0.5, 0.3 };
+	double factor = 0.709;
+	int minSize = 40;
+	std::string proto_model_dir = argv[1];
+	MTCNN *detector = new MTCNN(proto_model_dir);
+	int imgNum = imgNames.size();
+	for (int iNum = 0; iNum < imgNum; iNum++) {
+
+		cout << endl << iNum << " " << imgNames[iNum].c_str() << endl;
+		cv::VideoCapture capture(imgNames[iNum].c_str());
+		//cv::VideoCapture capture(0);
+		//capture.set(CV_CAP_PROP_FRAME_WIDTH, 1280);
+		//capture.set(CV_CAP_PROP_FRAME_HEIGHT, 720);
+		//
+		//VideoCapture capture("F:\\MTCNN-master\\vs2013_caffe_BN_multi_label\\water_meter_caffe_old\\\img\\1.avi");
+		//����Ƿ�������:�ɹ���ʱ��isOpened����ture  
+		if (!capture.isOpened())
+			cout << "fail to open!" << endl;
+		//��ȡ����֡��  
+		//	long totalFrameNumber = capture.get(CV_CAP_PROP_FRAME_COUNT);
+		//	cout << "������Ƶ��" << totalFrameNumber << "֡" << endl;
+		//
+		//
+		//���ÿ�ʼ֡()  
+		//	long frameToStart = 300;
+		//	capture.set(CV_CAP_PROP_POS_FRAMES, frameToStart);
+		//	cout << "�ӵ�" << frameToStart << "֡��ʼ��" << endl;
+		//
+		//
+		//���ý���֡  
+		//	int frameToStop = 400000;
+		//
+		//	if (frameToStop < frameToStart)
+		//	{
+		//		cout << "����֡С�ڿ�ʼ֡��������󣬼����˳���" << endl;
+		//		return -1;
+		//	}
+		//	else
+		//	{
+		//		cout << "����֡Ϊ����" << frameToStop << "֡" << endl;
+		//	}
+		//
+		//
+		//	��ȡ֡��  
+		//	double rate = capture.get(CV_CAP_PROP_FPS);
+		//	cout << "֡��Ϊ:" << rate << endl;
+
+
+
+		//����һ���������ƶ�ȡ��Ƶѭ�������ı���  
+		bool stop = false;
+
+		//��ʾÿһ֡�Ĵ���  
+		cv::namedWindow("Extracted frame_", 0);
+		//��֡��ļ��ʱ��:  
+		//int delay = 1000/rate;  
+		//	int delay = 1000 / rate;
+		//	if (rate == 0)  delay = 1;
+		//
+		//����whileѭ����ȡ֡  
+		//currentFrame����ѭ�����п��ƶ�ȡ��ָ����֡��ѭ�������ı���  
+		//	long currentFrame = frameToStart;
+		//
+		//    VideoWriter writer;
+		//    writer.open("../result/SuicideSquad.mp4",CV_FOURCC('M', 'J', 'P', 'G'), 25, Size(1280,720), true);
+		//����ÿһ֡��ͼ��  
+
+		cv::Mat image;
+		int frame_count = 0;
+		while (!stop)
+		{
+			//��ȡ��һ֡  
+			if (!capture.read(image))
+			{
+				cout << "��ȡ��Ƶʧ��" << endl;
+				stop = true;
+				continue;
+			}
+
+			//imshow("Live", image);
+			//waitKey(0);
+			//for (int i = 0; i < 100; i++)capture.read(image);
+			//
+			//cv::flip(image, image,-1);
+
+			std::vector<FaceInfo> faceInfo;
+			clock_t t1 = clock();
+			//		std::cout << "Detect " << image.rows << "X" << image.cols;
+			//
+			//image = image.t();
+
+			detector->Detect(image, faceInfo, minSize, threshold, factor);
+#ifdef CPU_ONLY
+			std::cout << " Time Using CPU: " << (clock() - t1)*1.0 / 1000 << std::endl;
+#else
+			std::cout << " Time Using GPU-CUDNN: " << (clock() - t1)*1.0 / 1000 << std::endl;
+#endif
+			cv::Mat dst_face;
+
+			for (int i = 0; i < faceInfo.size(); i++) {
+
+				float x = faceInfo[i].bbox.x1;
+				float y = faceInfo[i].bbox.y1;
+				float w = faceInfo[i].bbox.x2 - faceInfo[i].bbox.x1 + 1;
+				float h = faceInfo[i].bbox.y2 - faceInfo[i].bbox.y1 + 1;
+
+				if (x < 0) x = 0;	if (y < 0) y = 0;
+				if ((y + h) > image.rows) h = image.rows - y;
+				if ((x + w) > image.cols) w = image.cols - x;
+				if (w < 0) continue;
+				if (h < 0) continue;
+
+				std::cout << x << " " << y << " " << w << " " << h << std::endl;
+				std::cout << image.rows << " " << image.cols << std::endl;
+
+				dst_face = image(cv::Rect(x, y, w, h));
+				char dst_name[100];
+				_mkdir("face");
+				sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, ".jpg");
+				cv::imwrite(dst_name, dst_face);
+
+
+				FacePts facePts = faceInfo[i].facePts;
+
+
+
+
+
+				cv::Point2f srcTri[4];
+				cv::Point2f dstTri[4];
+				cv::Mat rot_mat(2, 4, CV_32FC1);
+				cv::Mat warp_mat(2, 4, CV_32FC1);
+
+				for (int j = 0; j < 4; j++) {
+					srcTri[j] = cv::Point2f(facePts.x[j] - x, facePts.y[j] - y);
+				}
+
+				int padding_x = cvFloor(h * 0.04 * 5);
+				int padding_y = cvFloor(h * 0.04 * 2);
+				int x0 = 0;		int y0 = 0;
+				int x1 = 120;	int y1 = 0;
+				int x2 = 120;	int y2 = 48;
+				int x3 = 0;		int y3 = 48;
+
+				dstTri[0] = cv::Point2f(x0 + padding_x, y0 + padding_y);
+				dstTri[1] = cv::Point2f(x1 + padding_x, y1 + padding_y);
+				dstTri[2] = cv::Point2f(x2 + padding_x, y2 + padding_y);
+				dstTri[3] = cv::Point2f(x3 + padding_x, y3 + padding_y);
+
+				warp_mat = cv::getAffineTransform(srcTri, dstTri);
+				cv::Mat warp_dstImage = cv::Mat::zeros(48 + 2 * padding_y, 120 + 2 * padding_x, dst_face.type());
+				cv::warpAffine(dst_face, warp_dstImage, warp_mat, warp_dstImage.size());
+
+#ifdef showSteps
+				cv::namedWindow("dst_face", 0);
+				cv::imshow("dst_face", dst_face);
+
+				cv::namedWindow("warp_dstImage", 0);
+				cv::imshow("warp_dstImage", warp_dstImage);
+
+				cv::waitKey(1);
+#endif
+
+				sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, "_warp_dstImage_.jpg");
+				cv::imwrite(dst_name, warp_dstImage);
+
+#ifdef warpPerspective
+
+				int img_height = dst_face.rows;
+				int img_width = dst_face.cols;
+
+				vector<Point2f> corners(4);
+
+				for (int j = 0; j < 4; j++) {
+					corners[j] = cv::Point2f(facePts.x[j] - x, facePts.y[j] - y);
+				}
+
+				vector<Point2f> corners_trans(4);
+				corners_trans[0] = cv::Point2f(x0 + padding_x, y0 + padding_y);
+				corners_trans[1] = cv::Point2f(x1 + padding_x, y1 + padding_y);
+				corners_trans[2] = cv::Point2f(x2 + padding_x, y2 + padding_y);
+				corners_trans[3] = cv::Point2f(x3 + padding_x, y3 + padding_y);
+
+				getStartTime();
+				Mat transform = getPerspectiveTransform(corners, corners_trans);
+				//cout << transform << endl;
+				Mat resultImage;
+				warpPerspective(dst_face, resultImage, transform, Size(120 + 2 * padding_x, 48 + 2 * padding_y), INTER_LINEAR);
+
+				getEndTime();
+				std::cout << " 2 :" << dfTim << std::endl;
+
+				namedWindow("warpPerspective", 0);
+				imshow("warpPerspective", resultImage);
+
+				cv::waitKey(1);
+
+
+
+				sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, "_resultImage_.jpg");
+				cv::imwrite(dst_name, resultImage);
+
+#endif
+
+
+
+
+
+#ifdef showSteps
+				for (int j = 0; j < 4; j++) {
+					std::cout << facePts.x[j] - x << " " << facePts.y[j] - y << std::endl;
+					cv::circle(image, cv::Point(facePts.x[j], facePts.y[j]), 1, cv::Scalar(255, 255, 0), 2);
+				}
+
+				cv::rectangle(image, cv::Rect(x, y, w, h), cv::Scalar(255, 0, 0), 2);
+				std::cout << x << " " << y << " " << w << " " << h << std::endl;
+#endif
+			}
+
+			cv::imshow("Extracted frame_", image);
+
+			int c = cv::waitKey(0);
+			//����ESC���ߵ���ָ���Ľ���֡���˳���ȡ��Ƶ  
+			if ((char)c == 27 /*|| currentFrame > frameToStop*/)
+			{
+				stop = true;
+			}
+			//���°������ͣ���ڵ�ǰ֡���ȴ���һ�ΰ���  
+			//if (c >= 0)
+			//{
+			//	waitKey(0);
+			//}
+			//	currentFrame++;
+
+		}
+
+		//�ر���Ƶ�ļ�  
+		capture.release();
+	}
+	return 0;
+}
+
+
+int main(int argc, char **argv) {
+	
+
+	
+	double threshold[3] = { 0.7, 0.8, 0.8 };
+	double factor = 0.709;
+	int minSize = 40;
+	std::string proto_model_dir = "..\\..\\..\\vs2013_caffe_BN_multi_label_kenel_w\\model_platecar\\";
+	MTCNN *detector = new MTCNN(proto_model_dir);
+
+
+
+
+#ifdef CPU_ONLY
+	bool usegpu = false;
+#else
+	bool usegpu = true;
+#endif
+
+	//load model
+	string modelfolder = ".\\plateCard_test\\";
+	ICNNPredict* pCNN = CreatePredictInstance(modelfolder.c_str(), usegpu);
+	int wstd = 0, hstd = 0;
+	pCNN->GetInputImageSize(wstd, hstd);
+
+	//get alphabet
+	vector<string> alphabets = pCNN->GetLabels();
+
+	int idxBlank = 0;
+	vector<string>::const_iterator it = find(alphabets.begin(), alphabets.end(), "blank");
+	if (it != alphabets.end())
+		idxBlank = (int)(it - alphabets.begin());
+
+
+	map<wchar_t, int> mapLabel2IDs;
+	for (size_t i = 0; i < alphabets.size(); i++)
+	{
+		wchar_t c = 0;
+		if (alphabets[i] == "blank")
+			continue;
+		wstring wlabel = string2wstring(alphabets[i], true);
+		mapLabel2IDs.insert(make_pair(wlabel[0], i));
+	}
+
+
+	int sumspend = 0;
+	int nok_lexicon = 0;
+	int nok_nolexicon = 0;
+
+
+
+
+	initTrainImage();
+	int imgNum = imgNames.size();
+	int *idx = new int[imgNum];
+
+	for (int i = 0; i < imgNum; i++) {
+		idx[i] = i;
+	}
+
+	RandomizeIdx(idx, imgNum);
+	for (int i = 0; i < imgNum; i++) {
+		int iNum = idx[i];
+		cout << endl << iNum << " " << imgNames[iNum].c_str() << endl;
+		cv::VideoCapture capture(imgNames[iNum].c_str());
+		//cv::VideoCapture capture(0);
+		//capture.set(CV_CAP_PROP_FRAME_WIDTH, 1280);
+		//capture.set(CV_CAP_PROP_FRAME_HEIGHT, 720);
+		//
+		//VideoCapture capture("F:\\MTCNN-master\\vs2013_caffe_BN_multi_label\\water_meter_caffe_old\\\img\\1.avi");
+		//����Ƿ�������:�ɹ���ʱ��isOpened����ture  
+		if (!capture.isOpened())
+			cout << "fail to open!" << endl;
+		//��ȡ����֡��  
+		//	long totalFrameNumber = capture.get(CV_CAP_PROP_FRAME_COUNT);
+		//	cout << "������Ƶ��" << totalFrameNumber << "֡" << endl;
+		//
+		//
+		//���ÿ�ʼ֡()  
+		//	long frameToStart = 300;
+		//	capture.set(CV_CAP_PROP_POS_FRAMES, frameToStart);
+		//	cout << "�ӵ�" << frameToStart << "֡��ʼ��" << endl;
+		//
+		//
+		//���ý���֡  
+		//	int frameToStop = 400000;
+		//
+		//	if (frameToStop < frameToStart)
+		//	{
+		//		cout << "����֡С�ڿ�ʼ֡��������󣬼����˳���" << endl;
+		//		return -1;
+		//	}
+		//	else
+		//	{
+		//		cout << "����֡Ϊ����" << frameToStop << "֡" << endl;
+		//	}
+		//
+		//
+		//	��ȡ֡��  
+		//	double rate = capture.get(CV_CAP_PROP_FPS);
+		//	cout << "֡��Ϊ:" << rate << endl;
+
+
+
+		//����һ���������ƶ�ȡ��Ƶѭ�������ı���  
+		bool stop = false;
+
+		//��ʾÿһ֡�Ĵ���  
+
+		//��֡��ļ��ʱ��:  
+		//int delay = 1000/rate;  
+		//	int delay = 1000 / rate;
+		//	if (rate == 0)  delay = 1;
+		//
+		//����whileѭ����ȡ֡  
+		//currentFrame����ѭ�����п��ƶ�ȡ��ָ����֡��ѭ�������ı���  
+		//	long currentFrame = frameToStart;
+		//
+		//    VideoWriter writer;
+		//    writer.open("../result/SuicideSquad.mp4",CV_FOURCC('M', 'J', 'P', 'G'), 25, Size(1280,720), true);
+		//����ÿһ֡��ͼ��  
+
+		cv::Mat image;
+		int frame_count = 0;
+		while (!stop)
+		{
+			//��ȡ��һ֡  
+			if (!capture.read(image))
+			{
+				cout << "��ȡ��Ƶʧ��" << endl;
+				stop = true;
+				continue;
+			}
+
+			//imshow("Live", image);
+			//waitKey(0);
+			//for (int i = 0; i < 100; i++)capture.read(image);
+			////
+			//cv::flip(image, image,-1);
+
+			std::vector<FaceInfo> faceInfo;
+			clock_t t1 = clock();
+			//		std::cout << "Detect " << image.rows << "X" << image.cols;
+			//
+			//image = image.t();
+
+			
+			cv::Mat mergeImg;//�ϲ����ͼ��  
+						 //�����洢��ͨ��ͼƬ������  
+			vector<cv::Mat> splitBGR(image.channels());
+			//�ָ�ͨ�����洢��splitBGR��  
+			split(image, splitBGR);
+			//�Ը���ͨ���ֱ����ֱ��ͼ���⻯  
+			for (int i = 0; i<image.channels(); i++)
+				equalizeHist(splitBGR[i], splitBGR[i]);
+			//�ϲ�ͨ��  
+			merge(splitBGR, mergeImg);
+
+			detector->Detect(mergeImg, faceInfo, minSize, threshold, factor);
+#ifdef CPU_ONLY
+			std::cout << " Time Using CPU: " << (clock() - t1)*1.0 / 1000 << std::endl;
+#else
+			std::cout << " Time Using : " << (clock() - t1)*1.0 / 1000 << std::endl;
+#endif
+			cv::Mat dst_face;
+
+			for (int i = 0; i < faceInfo.size(); i++) {
+
+				float x = faceInfo[i].bbox.x1;
+				float y = faceInfo[i].bbox.y1;
+				float w = faceInfo[i].bbox.x2 - faceInfo[i].bbox.x1 + 1;
+				float h = faceInfo[i].bbox.y2 - faceInfo[i].bbox.y1 + 1;
+
+				std::cout << "[" << i << "]�÷֣� " << faceInfo[i].bbox.score << std::endl;
+
+				if (x < 0) x = 0;	if (y < 0) y = 0;
+				if ((y + h) > image.rows) h = image.rows - y;
+				if ((x + w) > image.cols) w = image.cols - x;
+				if (w < 0) continue;
+				if (h < 0) continue;
+
+				//std::cout << x << " " << y << " " << w << " " << h << std::endl;
+				//std::cout << image.rows << " " << image.cols << std::endl;
+
+				dst_face = image(cv::Rect(x, y, w, h));
+				char dst_name[100];
+				_mkdir("C:\\plate_card_BLSTM\\testData\\face\\");
+				sprintf_s(dst_name, "%s%d%s%d%s%d%s", "C:\\plate_card_BLSTM\\testData\\face\\", iNum, "_", frame_count++, "_", i, ".jpg");
+				cv::imwrite(dst_name, dst_face);
+
+
+				FacePts facePts = faceInfo[i].facePts;
+
+
+
+
+
+				cv::Point2f srcTri[4];
+				cv::Point2f dstTri[4];
+				cv::Mat rot_mat(2, 4, CV_32FC1);
+				cv::Mat warp_mat(2, 4, CV_32FC1);
+
+				for (int j = 0; j < 4; j++) {
+					srcTri[j] = cv::Point2f(facePts.x[j] - x, facePts.y[j] - y);
+				}
+
+				int padding_x = cvFloor(h * 0.04 * 5);
+				int padding_y = cvFloor(h * 0.04 * 2);
+				int x0 = 0;		int y0 = 0;
+				int x1 = 120;	int y1 = 0;
+				int x2 = 120;	int y2 = 48;
+				int x3 = 0;		int y3 = 48;
+
+				dstTri[0] = cv::Point2f(x0 + padding_x, y0 + padding_y);
+				dstTri[1] = cv::Point2f(x1 + padding_x, y1 + padding_y);
+				dstTri[2] = cv::Point2f(x2 + padding_x, y2 + padding_y);
+				dstTri[3] = cv::Point2f(x3 + padding_x, y3 + padding_y);
+
+				warp_mat = cv::getAffineTransform(srcTri, dstTri);
+				cv::Mat warp_dstImage = cv::Mat::zeros(48 + 2 * padding_y, 120 + 2 * padding_x, dst_face.type());
+				cv::warpAffine(dst_face, warp_dstImage, warp_mat, warp_dstImage.size());
+
+#ifdef showSteps
+				cv::namedWindow("dst_face", 0);
+				cv::imshow("dst_face", dst_face);
+
+				cv::namedWindow("warp_dstImage", 0);
+				cv::imshow("warp_dstImage", warp_dstImage);
+
+				cv::waitKey(1);
+#endif
+
+				sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, "_warp_dstImage_.jpg");
+				cv::imwrite(dst_name, warp_dstImage);
+
+#ifdef warpPerspective
+
+				int img_height = dst_face.rows;
+				int img_width = dst_face.cols;
+
+				vector<Point2f> corners(4);
+
+				for (int j = 0; j < 4; j++) {
+					corners[j] = cv::Point2f(facePts.x[j] - x, facePts.y[j] - y);
+				}
+
+				vector<Point2f> corners_trans(4);
+				corners_trans[0] = cv::Point2f(x0 + padding_x, y0 + padding_y);
+				corners_trans[1] = cv::Point2f(x1 + padding_x, y1 + padding_y);
+				corners_trans[2] = cv::Point2f(x2 + padding_x, y2 + padding_y);
+				corners_trans[3] = cv::Point2f(x3 + padding_x, y3 + padding_y);
+
+				getStartTime();
+				Mat transform = getPerspectiveTransform(corners, corners_trans);
+				//cout << transform << endl;
+				Mat resultImage;
+				warpPerspective(dst_face, resultImage, transform, Size(120 + 2 * padding_x, 48 + 2 * padding_y), INTER_LINEAR);
+
+				getEndTime();
+				std::cout << " 2 :" << dfTim << std::endl;
+
+				namedWindow("warpPerspective", 0);
+				imshow("warpPerspective", resultImage);
+
+				cv::waitKey(1);
+
+
+
+				sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, "_resultImage_.jpg");
+				cv::imwrite(dst_name, resultImage);
+
+#endif
+				cv::Mat img = warp_dstImage;
+				cv::normalize(img, img, 255, 0, cv::NORM_MINMAX);
+				int w_rec = img.cols, h_rec = img.rows;
+				if (2 * w_rec <= h_rec)
+				{
+					cv::transpose(img, img);
+					cv::flip(img, img, 1);
+					w_rec = img.cols, h_rec = img.rows;
+				}
+
+
+				//int w1 = hstd*w / h;
+				int w1 = 280;
+				if (w1 != w_rec && h_rec != hstd)
+					cv::resize(img, img, cv::Size(w1, hstd), cv::INTER_CUBIC);
+
+				int start = clock();
+
+				vector<int> shape;
+				vector<float> pred = pCNN->GetOutputFeatureMap(img, shape);
+
+				int end = clock();
+				sumspend += (end - start);
+
+				string strpredict0 = GetPredictString(pred, idxBlank, alphabets);
+
+				printf("[%d]%s\n", i, strpredict0.c_str());
+#ifdef showSteps
+				cv::namedWindow("img", 0);
+				cv::imshow("img", img);
+				cv::waitKey(1);
+#endif
+
+
+#ifdef showSteps
+				for (int j = 0; j < 4; j++) {
+				//	std::cout << facePts.x[j] - x << " " << facePts.y[j] - y << std::endl;
+					cv::circle(image, cv::Point(facePts.x[j], facePts.y[j]), 1, cv::Scalar(255, 255, 0), 2);
+				}
+
+				cv::rectangle(image, cv::Rect(x, y, w, h), cv::Scalar(255, 0, 0), 2);
+				//std::cout << x << " " << y << " " << w << " " << h << std::endl;
+#endif
+			}
+
+			if (faceInfo.size() == 0) {
+				cv::Mat img = image;
+				//cv::normalize(img, img, 255, 0, cv::NORM_MINMAX);
+				int w_rec = img.cols, h_rec = img.rows;
+				if (2 * w_rec <= h_rec)
+				{
+					cv::transpose(img, img);
+					cv::flip(img, img, 1);
+					w_rec = img.cols, h_rec = img.rows;
+				}
+
+
+				//int w1 = hstd*w / h;
+				int w1 = 280;
+				if (w1 != w_rec && h_rec != hstd)
+					cv::resize(img, img, cv::Size(w1, hstd),cv::INTER_CUBIC);
+
+				int start = clock();
+
+				vector<int> shape;
+				vector<float> pred = pCNN->GetOutputFeatureMap(img, shape);
+
+				int end = clock();
+				sumspend += (end - start);
+
+				string strpredict0 = GetPredictString(pred, idxBlank, alphabets);
+
+				printf("[%d]%s\n", iNum, strpredict0.c_str());
+
+			}
+
+
+			cv::namedWindow("Extracted frame_", 0);
+			cv::imshow("Extracted frame_", image);
+
+			int c = cv::waitKey(0);
+			//����ESC���ߵ���ָ���Ľ���֡���˳���ȡ��Ƶ  
+			if ((char)c == 27 /*|| currentFrame > frameToStop*/)
+			{
+				stop = true;
+			}
+			//���°������ͣ���ڵ�ǰ֡���ȴ���һ�ΰ���  
+			//if (c >= 0)
+			//{
+			//	waitKey(0);
+			//}
+			//	currentFrame++;
+
+		}
+
+		//�ر���Ƶ�ļ�  
+		capture.release();
+	}
+	return 0;
+
+
+}
+
+
+int main_mtcnn_haar(int argc, char **argv) {
+
+
+
+	double threshold[3] = { 0.7, 0.8, 0.8 };
+	double factor = 0.709;
+	int minSize = 40;
+	std::string proto_model_dir = "C:\\plate_card_BLSTM\\vs2013_caffe_BN_multi_label_kenel_w\\model_platecar\\";
+	MTCNN *detector = new MTCNN(proto_model_dir);
+
+#ifndef HAAR_CASCADE
+	cv::CascadeClassifier char_cascade;
+	if (!char_cascade.load(".\\rec_test\\cascade12.xml"))
+	{
+		cerr << "ERROR: Could not load classifier cascade" << endl;
+		return -1;
+	}
+
+
+	string chinese_model_file = ".\\rec_test\\chinese\\model.prototxt";
+	string chinese_trained_file = ".\\rec_test\\chinese\\model.caffemodel";
+	string chinese_mean_file = "";
+	int chinese_label_file = 31;
+	Classifier net_chinese(chinese_model_file, chinese_trained_file, chinese_mean_file, chinese_label_file);
+
+
+	string char_model_file = ".\\rec_test\\char\\model.prototxt";
+	string char_trained_file = ".\\rec_test\\char\\model.caffemodel";
+	string char_mean_file = "";
+	int char_label_file = 37;
+	Classifier net_char(char_model_file, char_trained_file, char_mean_file, char_label_file);
+
+#endif // HAAR_CASCADE
+
+	
+
+
+#ifdef CPU_ONLY
+	bool usegpu = false;
+#else
+	bool usegpu = true;
+#endif
+
+	//load model
+	string modelfolder = ".\\plateCard_test\\";
+	ICNNPredict* pCNN = CreatePredictInstance(modelfolder.c_str(), usegpu);
+	int wstd = 0, hstd = 0;
+	pCNN->GetInputImageSize(wstd, hstd);
+
+	//get alphabet
+	vector<string> alphabets = pCNN->GetLabels();
+
+	int idxBlank = 0;
+	vector<string>::const_iterator it = find(alphabets.begin(), alphabets.end(), "blank");
+	if (it != alphabets.end())
+		idxBlank = (int)(it - alphabets.begin());
+
+
+	map<wchar_t, int> mapLabel2IDs;
+	for (size_t i = 0; i < alphabets.size(); i++)
+	{
+		wchar_t c = 0;
+		if (alphabets[i] == "blank")
+			continue;
+		wstring wlabel = string2wstring(alphabets[i], true);
+		mapLabel2IDs.insert(make_pair(wlabel[0], i));
+	}
+
+
+	int sumspend = 0;
+	int nok_lexicon = 0;
+	int nok_nolexicon = 0;
+
+
+
+
+	initTrainImage();
+	int imgNum = imgNames.size();
+	for (int iNum = 0; iNum < imgNum; iNum++) {
+
+		cout << endl << iNum << " " << imgNames[iNum].c_str() << endl;
+		cv::VideoCapture capture(imgNames[iNum].c_str());
+		//cv::VideoCapture capture(0);
+		//capture.set(CV_CAP_PROP_FRAME_WIDTH, 1280);
+		//capture.set(CV_CAP_PROP_FRAME_HEIGHT, 720);
+		//
+		//VideoCapture capture("F:\\MTCNN-master\\vs2013_caffe_BN_multi_label\\water_meter_caffe_old\\\img\\1.avi");
+		//����Ƿ�������:�ɹ���ʱ��isOpened����ture  
+		if (!capture.isOpened())
+			cout << "fail to open!" << endl;
+		//��ȡ����֡��  
+		//	long totalFrameNumber = capture.get(CV_CAP_PROP_FRAME_COUNT);
+		//	cout << "������Ƶ��" << totalFrameNumber << "֡" << endl;
+		//
+		//
+		//���ÿ�ʼ֡()  
+		//	long frameToStart = 300;
+		//	capture.set(CV_CAP_PROP_POS_FRAMES, frameToStart);
+		//	cout << "�ӵ�" << frameToStart << "֡��ʼ��" << endl;
+		//
+		//
+		//���ý���֡  
+		//	int frameToStop = 400000;
+		//
+		//	if (frameToStop < frameToStart)
+		//	{
+		//		cout << "����֡С�ڿ�ʼ֡��������󣬼����˳���" << endl;
+		//		return -1;
+		//	}
+		//	else
+		//	{
+		//		cout << "����֡Ϊ����" << frameToStop << "֡" << endl;
+		//	}
+		//
+		//
+		//	��ȡ֡��  
+		//	double rate = capture.get(CV_CAP_PROP_FPS);
+		//	cout << "֡��Ϊ:" << rate << endl;
+
+
+
+		//����һ���������ƶ�ȡ��Ƶѭ�������ı���  
+		bool stop = false;
+
+		//��ʾÿһ֡�Ĵ���  
+
+		//��֡��ļ��ʱ��:  
+		//int delay = 1000/rate;  
+		//	int delay = 1000 / rate;
+		//	if (rate == 0)  delay = 1;
+		//
+		//����whileѭ����ȡ֡  
+		//currentFrame����ѭ�����п��ƶ�ȡ��ָ����֡��ѭ�������ı���  
+		//	long currentFrame = frameToStart;
+		//
+		//    VideoWriter writer;
+		//    writer.open("../result/SuicideSquad.mp4",CV_FOURCC('M', 'J', 'P', 'G'), 25, Size(1280,720), true);
+		//����ÿһ֡��ͼ��  
+
+		cv::Mat image;
+		int frame_count = 0;
+		while (!stop)
+		{
+			//��ȡ��һ֡  
+			if (!capture.read(image))
+			{
+				cout << "��ȡ��Ƶʧ��" << endl;
+				stop = true;
+				continue;
+			}
+
+			//imshow("Live", image);
+			//waitKey(0);
+			//for (int i = 0; i < 100; i++)capture.read(image);
+			////
+			//cv::flip(image, image,-1);
+
+			std::vector<FaceInfo> faceInfo;
+			clock_t t1 = clock();
+			//		std::cout << "Detect " << image.rows << "X" << image.cols;
+			//
+			//image = image.t();
+
+
+			cv::Mat mergeImg;//�ϲ����ͼ��  
+							 //�����洢��ͨ��ͼƬ������  
+			vector<cv::Mat> splitBGR(image.channels());
+			//�ָ�ͨ�����洢��splitBGR��  
+			split(image, splitBGR);
+			//�Ը���ͨ���ֱ����ֱ��ͼ���⻯  
+			for (int i = 0; i<image.channels(); i++)
+				equalizeHist(splitBGR[i], splitBGR[i]);
+			//�ϲ�ͨ��  
+			merge(splitBGR, mergeImg);
+
+			detector->Detect(mergeImg, faceInfo, minSize, threshold, factor);
+#ifdef CPU_ONLY
+			std::cout << " Time Using CPU: " << (clock() - t1)*1.0 / 1000 << std::endl;
+#else
+			std::cout << " Time Using : " << (clock() - t1)*1.0 / 1000 << std::endl;
+#endif
+			cv::Mat dst_face;
+
+#ifndef HAAR_CASCADE
+			vector<result_plate> results;
+			vector<result_plate>(results).swap(results);
+			results.clear();
+#endif // DEBUG
+
+			cv::Mat warp_dstImage;
+			for (int i = 0; i < faceInfo.size() ||(i==0 && faceInfo.size() ==0); i++) {
+				
+				if (faceInfo.size() != 0) {
+
+					float x = faceInfo[i].bbox.x1;
+					float y = faceInfo[i].bbox.y1;
+					float w = faceInfo[i].bbox.x2 - faceInfo[i].bbox.x1 + 1;
+					float h = faceInfo[i].bbox.y2 - faceInfo[i].bbox.y1 + 1;
+
+					std::cout << "[" << i << "]�÷֣� " << faceInfo[i].bbox.score << std::endl;
+
+					if (x < 0) x = 0;	if (y < 0) y = 0;
+					if ((y + h) > image.rows) h = image.rows - y;
+					if ((x + w) > image.cols) w = image.cols - x;
+					if (w < 0) continue;
+					if (h < 0) continue;
+
+					//std::cout << x << " " << y << " " << w << " " << h << std::endl;
+					//std::cout << image.rows << " " << image.cols << std::endl;
+
+					dst_face = image(cv::Rect(x, y, w, h));
+					char dst_name[100];
+					_mkdir("C:\\plate_card_BLSTM\\testData\\face\\");
+					sprintf_s(dst_name, "%s%d%s%d%s%d%s", "C:\\plate_card_BLSTM\\testData\\face\\", iNum, "_", frame_count++, "_", i, ".jpg");
+					cv::imwrite(dst_name, dst_face);
+
+
+					FacePts facePts = faceInfo[i].facePts;
+
+
+
+
+
+					cv::Point2f srcTri[4];
+					cv::Point2f dstTri[4];
+					cv::Mat rot_mat(2, 4, CV_32FC1);
+					cv::Mat warp_mat(2, 4, CV_32FC1);
+
+					for (int j = 0; j < 4; j++) {
+						srcTri[j] = cv::Point2f(facePts.x[j] - x, facePts.y[j] - y);
+					}
+
+					int padding_x = cvFloor(h * 0.04 * 5);
+					int padding_y = cvFloor(h * 0.04 * 2);
+					int x0 = 0;		int y0 = 0;
+					int x1 = 120;	int y1 = 0;
+					int x2 = 120;	int y2 = 48;
+					int x3 = 0;		int y3 = 48;
+
+					dstTri[0] = cv::Point2f(x0 + padding_x, y0 + padding_y);
+					dstTri[1] = cv::Point2f(x1 + padding_x, y1 + padding_y);
+					dstTri[2] = cv::Point2f(x2 + padding_x, y2 + padding_y);
+					dstTri[3] = cv::Point2f(x3 + padding_x, y3 + padding_y);
+
+					warp_mat = cv::getAffineTransform(srcTri, dstTri);
+					/*cv::Mat */warp_dstImage = cv::Mat::zeros(48 + 2 * padding_y, 120 + 2 * padding_x, dst_face.type());
+					cv::warpAffine(dst_face, warp_dstImage, warp_mat, warp_dstImage.size());
+
+#ifdef showSteps
+					cv::namedWindow("dst_face", 0);
+					cv::imshow("dst_face", dst_face);
+
+					cv::namedWindow("warp_dstImage", 0);
+					cv::imshow("warp_dstImage", warp_dstImage);
+
+					cv::waitKey(1);
+#endif
+
+					sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, "_warp_dstImage_.jpg");
+					cv::imwrite(dst_name, warp_dstImage);
+
+#ifdef warpPerspective
+
+					int img_height = dst_face.rows;
+					int img_width = dst_face.cols;
+
+					vector<Point2f> corners(4);
+
+					for (int j = 0; j < 4; j++) {
+						corners[j] = cv::Point2f(facePts.x[j] - x, facePts.y[j] - y);
+					}
+
+					vector<Point2f> corners_trans(4);
+					corners_trans[0] = cv::Point2f(x0 + padding_x, y0 + padding_y);
+					corners_trans[1] = cv::Point2f(x1 + padding_x, y1 + padding_y);
+					corners_trans[2] = cv::Point2f(x2 + padding_x, y2 + padding_y);
+					corners_trans[3] = cv::Point2f(x3 + padding_x, y3 + padding_y);
+
+					getStartTime();
+					Mat transform = getPerspectiveTransform(corners, corners_trans);
+					//cout << transform << endl;
+					Mat resultImage;
+					warpPerspective(dst_face, resultImage, transform, Size(120 + 2 * padding_x, 48 + 2 * padding_y), INTER_LINEAR);
+
+					getEndTime();
+					std::cout << " 2 :" << dfTim << std::endl;
+
+					namedWindow("warpPerspective", 0);
+					imshow("warpPerspective", resultImage);
+
+					cv::waitKey(1);
+
+
+
+					sprintf_s(dst_name, "%s%d%s%d%s%d%s", "face\\", iNum, "_", frame_count++, "_", i, "_resultImage_.jpg");
+					cv::imwrite(dst_name, resultImage);
+
+#endif
+				
+#ifdef showSteps
+					for (int j = 0; j < 4; j++) {
+						//	std::cout << facePts.x[j] - x << " " << facePts.y[j] - y << std::endl;
+						cv::circle(image, cv::Point(facePts.x[j], facePts.y[j]), 1, cv::Scalar(255, 255, 0), 2);
+					}
+
+					cv::rectangle(image, cv::Rect(x, y, w, h), cv::Scalar(255, 0, 0), 2);
+					//std::cout << x << " " << y << " " << w << " " << h << std::endl;
+#endif
+
+				}
+				else if (faceInfo.size() == 0)
+				{
+					warp_dstImage = image;
+				}
+#ifndef HAAR_CASCADE
+				result_plate result;
+				cv::Rect face;
+				if (faceInfo.size() != 0) {
+					face = cv::Rect(faceInfo[i].bbox.x1, faceInfo[i].bbox.y1, faceInfo[i].bbox.x2 - faceInfo[i].bbox.x1, faceInfo[i].bbox.y2 - faceInfo[i].bbox.y1);
+				}
+				else if (faceInfo.size() == 0)
+				{
+					face = cv::Rect(0,0,image.cols,image.rows);
+					warp_dstImage = image;
+				}
+				result.coordinate = face;
+
+
+				cv::Mat detect_obj = warp_dstImage;
+				//cv::resize(detect_obj, detect_obj ,cv::Size(detect_obj.cols * 2, detect_obj.rows *2),cv::INTER_CUBIC);
+
+				vector<CvRect> detectROI;
+				cv::Mat show_detect_obj;	detect_obj.copyTo(show_detect_obj);
+				cv::Mat shaixuan_obj, choose_detect_obj, normalization_detect_obj;
+				shaixuan_obj = show_detect_obj.clone();//10-12
+				choose_detect_obj = shaixuan_obj.clone();
+				normalization_detect_obj = shaixuan_obj.clone();
+				//######################################################################	
+				std::vector<cv::Rect> chars_roi;
+				char_cascade.detectMultiScale(detect_obj, chars_roi, 1.05, 1, 0 | CV_HAAR_SCALE_IMAGE/*, cv::Size(0, 0), cv::Size(1700, 1700)*/);
+				if (showSteps)
+					printf("chars_roi size = %d \n", chars_roi.size());
+
+				for (unsigned int j = 0; j < chars_roi.size(); j++)
+				{
+					const cv::Rect& single_char_roi = chars_roi[j];
+					detectROI.push_back(single_char_roi);
+				}
+
+				std::sort(detectROI.begin(), detectROI.end(), sort_by_x);
+
+				if (showSteps) {
+					for (int i = 0; i < detectROI.size(); i++) {
+						cout << detectROI[i].x << " " << detectROI[i].y << " " << detectROI[i].width << " " << detectROI[i].height << endl;
+
+						cv::Point tl(detectROI[i].x, detectROI[i].y);
+						cv::Point br = tl + cv::Point(detectROI[i].width, detectROI[i].height);
+						cv::Scalar magenta = cv::Scalar((i) * 10, 255 - (i + 1) * 10, (i + 2) * 50);//��ɫѡȡ��
+
+						cv::rectangle(show_detect_obj, tl, br, magenta, 1, 1, 0);
+					}
+
+					cv::namedWindow("show_detect_obj", 0);
+					cv::imshow("show_detect_obj", show_detect_obj);
+
+				}
+
+				///**************** step 2.1 fix detected roi ******************************//
+				int avg_distance_of_chars = 0;
+				int ROIWIDTH = 0; //ƽ����
+				int ROIHEIGHT = 0;//ƽ����
+
+				notfound = 1;
+				if (showSteps)
+					printf(" \n start \n");
+
+				vector<CvRect> ROI_choose_paixu = roichoose(detectROI, choose_detect_obj);//ѡ����ʵĿ�
+
+				if (showSteps)
+					show_choose_step(shaixuan_obj, ROI_choose_paixu, "ROI_choose_paixu");
+				if (showSteps)
+					printf("ʹ������Ѱ�ҵ��Ŀ�����Ϊ%d \n", ROI_choose_paixu.size());
+
+				if (notfound == 0 || detectROI.size() < 4) {
+					printf(" ��⵽�ij����ַ�����Ϊ�� %d ������4�����˳� ... \n", detectROI.size());
+					continue;
+				}
+
+				vector<CvRect> ROI_normalization = roinormalization(ROI_choose_paixu, normalization_detect_obj);//��һ��
+
+				if (showSteps)
+					show_choose_step(shaixuan_obj, ROI_normalization, "ROI_normalization");
+
+				vector<CvRect> ROI_choose = roicomplete(ROI_normalization, normalization_detect_obj);//����
+
+				if (showSteps)
+					show_choose_step(shaixuan_obj, ROI_choose, "ROI_choose");
+
+				std::sort(ROI_choose.begin(), ROI_choose.end(), sort_by_x);
+
+				//show_choose_step(cv::Mat shaixuan_obj, vector<cv::Rect> ROI_choose, char* windName)
+
+				///**************************  ����ʶ��ɸѡ�� **************************/
+				IplImage *detect = &IplImage(normalization_detect_obj);
+				if (ROI_choose.size() > 4)
+				{
+					///??????????????????????			
+
+
+					if (ROI_choose.size() > 5)
+					{
+
+						/*******************************************��һ���ַ��ó���*******************************/
+						if (showSteps)
+							printf("����: ");
+						if (ROI_choose.size() == 8)//����ȥ��ǰ���Ǻ�(����һ��ʹ�ó����м�ĵ��λ�ý���ʶ���ڿ���ر���ʱ�򲻺��жϣ�
+						{
+
+							if (ROI_choose[2].x - ROI_choose[1].x - ROI_choose[1].width > 0 && ROI_choose[3].x - ROI_choose[2].x - ROI_choose[2].width > 0)
+							{
+								if (showSteps)
+									printf("special");
+							}
+							/*	else if (ROI_choose[2].x - ROI_choose[1].x - ROI_choose[1].width > 0)  //�������������
+							ROI_choose.erase(ROI_choose.begin() + 7);
+							else if (ROI_choose[3].x - ROI_choose[2].x - ROI_choose[2].width > 0)
+							ROI_choose.erase(ROI_choose.begin());*/
+						}
+
+						/// ********************* ����ȥ��ǰ���Ǻ�(��������ʹ��ʶ��������ȥ������������loss��׼ȷ�� ********************///
+						if (ROI_choose.size() == 8)
+						{
+							int predict0, predict1, predict11, predict7, predict6;
+							predict0 = -1;
+							predict1 = -1; predict11 = -1;
+							predict7 = -1;
+							double loss0, loss1, loss11, loss7, loss6;
+							loss0 = 0.0;
+							loss11 = 0.0;
+							loss1 = 0.0;
+							loss7 = 0.0;
+							CvRect cut_single0 = ROI_choose[0];
+							CvRect cut_single1 = ROI_choose[1];
+							CvRect cut_single7 = ROI_choose[7];
+							IplImage* img_single0_result = cvCreateImage(cvSize(cut_single0.width, cut_single0.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+							cvSetImageROI(detect, cut_single0);
+							cvCopy(detect, img_single0_result);
+							cvResetImageROI(detect);
+
+							IplImage* img_single1_result = cvCreateImage(cvSize(cut_single1.width, cut_single1.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+							cvSetImageROI(detect, cut_single1);
+							cvCopy(detect, img_single1_result);
+							cvResetImageROI(detect);
+
+							IplImage* img_single7_result = cvCreateImage(cvSize(cut_single7.width, cut_single7.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+							cvSetImageROI(detect, cut_single7);
+							cvCopy(detect, img_single7_result);
+							cvResetImageROI(detect);
+
+							IplImage* img_gray0 = cvCreateImage(cvGetSize(img_single0_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+							cvCvtColor(img_single0_result, img_gray0, CV_BGR2GRAY);
+							cv::Mat img_re0 = cv::cvarrToMat(img_gray0);
+							/*IplImage* img_re0 = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+							cvResize(img_gray0, img_re0);*/
+
+							IplImage* img_gray1 = cvCreateImage(cvGetSize(img_single1_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+							cvCvtColor(img_single1_result, img_gray1, CV_BGR2GRAY);
+							cv::Mat img_re1 = cv::cvarrToMat(img_gray1);
+							/*IplImage* img_re1 = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+							cvResize(img_gray1, img_re1);*/
+
+							IplImage* img_gray7 = cvCreateImage(cvGetSize(img_single7_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+							cvCvtColor(img_single7_result, img_gray7, CV_BGR2GRAY);
+							cv::Mat img_re7 = cv::cvarrToMat(img_gray7);
+							/*IplImage* img_re7 = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+							cvResize(img_gray7, img_re7);*/
+							rec_char(net_chinese, img_re0, predict0, loss0);
+							rec_char(net_chinese, img_re1, predict1, loss1);
+							rec_char(net_char, img_re1, predict11, loss11);
+							rec_char(net_char, img_re7, predict7, loss7);
+
+							//charRec0.recognise_form_memory_scale0_1(img_re0, &predict0, &loss0);//ʶ���һλ�Ƿ��Ǻ���
+							//charRec0.recognise_form_memory_scale0_1(img_re1, &predict1, &loss1);//�ڶ�λ�Ƿ��Ǻ���
+							//charRec.recognise_form_memory_scale0_1(img_re1, &predict11, &loss11);//�ڶ�λ�Ƿ�����ĸ
+							//charRec.recognise_form_memory_scale0_1(img_re7, &predict7, &loss7);//����λ�Ƿ�����ĸ
+
+							if (showSteps)
+								printf("loss0_cn=%f loss7_char=%f loss1_Chinese=%f loss1_char=%f \n", loss0, loss7, loss1, loss11);
+							if (loss11 > loss1) {
+								ROI_choose.erase(ROI_choose.begin() + 7);
+							}
+							else {
+								ROI_choose.erase(ROI_choose.begin());
+							}
+
+
+						}
+						/// *****************************  //�������ʱҲ���Դ���   ***********************// 
+						if (ROI_choose.size() == 9)
+						{
+							int predict1_cn, predict1_char, predict2_cn, predict2_char, predict3_cn, predict3_char = -1;
+
+							double loss1_cn, loss1_char, loss2_cn, loss2_char, loss3_cn, loss3_char = 0.0;
+
+							CvRect cut_single1 = ROI_choose[0];
+							CvRect cut_single2 = ROI_choose[1];
+							CvRect cut_single3 = ROI_choose[2];
+							IplImage* img_single1_result = cvCreateImage(cvSize(cut_single1.width, cut_single1.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+							cvSetImageROI(detect, cut_single1);
+							cvCopy(detect, img_single1_result);
+							cvResetImageROI(detect);
+
+							IplImage* img_single2_result = cvCreateImage(cvSize(cut_single2.width, cut_single2.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+							cvSetImageROI(detect, cut_single2);
+							cvCopy(detect, img_single2_result);
+							cvResetImageROI(detect);
+
+							IplImage* img_single3_result = cvCreateImage(cvSize(cut_single3.width, cut_single3.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+							cvSetImageROI(detect, cut_single3);
+							cvCopy(detect, img_single3_result);
+							cvResetImageROI(detect);
+
+							IplImage* img_gray1 = cvCreateImage(cvGetSize(img_single1_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+							cvCvtColor(img_single1_result, img_gray1, CV_BGR2GRAY);
+							cv::Mat img_re1 = cv::cvarrToMat(img_gray1);
+							/*IplImage* img_re1 = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+							cvResize(img_gray1, img_re1);*/
+
+							IplImage* img_gray2 = cvCreateImage(cvGetSize(img_single2_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+							cvCvtColor(img_single2_result, img_gray2, CV_BGR2GRAY);
+							cv::Mat img_re2 = cv::cvarrToMat(img_gray2);
+							/*IplImage* img_re2 = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+							cvResize(img_gray2, img_re2);*/
+
+							IplImage* img_gray3 = cvCreateImage(cvGetSize(img_single3_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+							cvCvtColor(img_single3_result, img_gray3, CV_BGR2GRAY);
+							cv::Mat img_re3 = cv::cvarrToMat(img_gray3);
+							/*IplImage* img_re3 = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+							cvResize(img_gray3, img_re3);*/
+
+
+							rec_char(net_chinese, img_re1, predict1_cn, loss1_cn);
+							rec_char(net_chinese, img_re2, predict2_cn, loss2_cn);
+							rec_char(net_chinese, img_re3, predict3_cn, loss3_cn);
+							rec_char(net_char, img_re1, predict1_char, loss1_char);
+							rec_char(net_char, img_re2, predict2_char, loss2_char);
+							rec_char(net_char, img_re3, predict3_char, loss3_char);
+
+							//charRec0.recognise_form_memory_scale0_1(img_re1, &predict1_cn, &loss1_cn);//ʶ���һλ�Ƿ��Ǻ���
+							//charRec0.recognise_form_memory_scale0_1(img_re2, &predict2_cn, &loss2_cn);//�ڶ�λ�Ƿ��Ǻ���
+							//charRec0.recognise_form_memory_scale0_1(img_re3, &predict3_cn, &loss3_cn);//����λ�Ƿ��Ǻ���
+							//charRec.recognise_form_memory_scale0_1(img_re1, &predict1_char, &loss1_char);//��һλ�Ƿ�����ĸ
+							//charRec.recognise_form_memory_scale0_1(img_re2, &predict2_char, &loss2_char);//�ڶ�λ�Ƿ�����ĸ
+							//charRec.recognise_form_memory_scale0_1(img_re3, &predict3_char, &loss3_char);//����λ�Ƿ�����ĸ
+
+							if (loss3_cn > loss3_char)//����λΪ����ʱ��ǰ��λɾ��
+							{
+								ROI_choose.erase(ROI_choose.begin());
+								ROI_choose.erase(ROI_choose.begin());
+							}
+							else if (loss2_cn > loss2_char) //�ڶ�λΪ����ʱ��ǰ���ɾһλ
+							{
+								ROI_choose.erase(ROI_choose.begin() + 8);
+								ROI_choose.erase(ROI_choose.begin());
+							}
+							else //����λ�ڶ�λ����Ϊ����ʱ��ɾ������λ
+							{
+								ROI_choose.erase(ROI_choose.begin() + 8);
+								ROI_choose.erase(ROI_choose.begin() + 7);
+							}
+
+							/*if (loss11 > loss1) {
+							ROI_choose.erase(ROI_choose.begin() + 7);
+							}
+							else {
+							ROI_choose.erase(ROI_choose.begin());
+							}*/
+							if (showSteps) {
+								printf("loss1Ϊroichoose�ĵ�һ���� \n");
+								printf("loss1_cn=%f loss1_char=%f \n loss2_cn=%f loss2_char=%f \n loss3_cn=%f loss3_char=%f \n", loss1_cn, loss1_char, loss2_cn, loss2_char, loss3_cn, loss3_char);
+							}
+						}
+						if (showSteps) {
+							show_choose_step(shaixuan_obj, ROI_choose, "ROI_choose_final");
+							//cvWaitKey(0);
+						}
+
+
+						///*****************************  ��������ʶ��  *********************** 4-25�����ж��Ƿ�ʹ��ȫ�����ij���*******************************/
+						if (ROI_choose.size() >= 6)
+						{
+
+							int use_fcn = 0;
+							int roiwidth = ROI_choose[0].width;
+							for (int i = 3; i < ROI_choose.size(); i++)
+							{
+								if (ROI_choose[i].x - roiwidth - ROI_choose[i - 1].x > 0.1*roiwidth)
+									use_fcn++;
+							}
+							//if (use_fcn >= 0)
+							//{
+							//	//printf("need use fcn \n");
+							//
+							//
+							//	std::vector<struct result_> predictions = classifier.Classify(detect_obj, 1);
+							//	for (int i = 0; i < predictions.size(); i++) {
+							//		std::cout << predictions[i].label << " ";
+							//		cv::Rect roi = cv::Rect(predictions[i].centor.x - predictions[i].avg_width / 2,
+							//			predictions[i].centor.y - predictions[i].avg_height / 2,
+							//			predictions[i].avg_width,
+							//			predictions[i].avg_height
+							//		);//200*80ͼƬ�µĿ�
+							//		cv::Rect roi_final;
+							//		roi_final.x = (roi.x*detect_obj.cols) / 200;
+							//		roi_final.y = (roi.y*detect_obj.rows) / 80;
+							//		roi_final.width = (roi.width*detect_obj.cols) / 200;
+							//		roi_final.height = (roi.height*detect_obj.rows) / 80;
+							//
+							//		cv::rectangle(detect_obj, roi_final, cv::Scalar(255, 0, 255), 1, 8, 0);
+							//	}
+							//
+							//
+							//	cv::namedWindow("src", 0);
+							//	cv::imshow("src", detect_obj);
+							//	cvWaitKey();
+							//	
+							//}
+							//if (use_fcn > 0) {
+							//	
+							//	_mkdir("use_fcn");
+							//
+							//	SYSTEMTIME stTime;
+							//	GetLocalTime(&stTime);
+							//	char pVideoName[256];
+							//	sprintf_s(pVideoName, 256, "\\%d_%d_%d_%d_%d_%d_%d", stTime.wYear, stTime.wMonth, stTime.wDay, stTime.wHour, stTime.wMinute, stTime.wSecond, stTime.wMilliseconds);
+							//	char image_name1[500];
+							//	sprintf_s(image_name1, 500, "%s%s%s%s", "use_fcn", "\\", pVideoName, ".jpg");//�����ͼƬ��
+							//	imwrite(image_name1, img_src_orignal);
+
+							//	continue;
+							//}
+							/************************��ʹ��fcn***************/
+							if (1) {
+								for (int i = 0; i < ROI_choose.size(); i++)
+								{
+									CvRect cut_single = ROI_choose[i];
+									IplImage* img_single_result = cvCreateImage(cvSize(cut_single.width, cut_single.height), detect->depth, detect->nChannels);//�õ����ַ���ֵͼ��
+									cvSetImageROI(detect, cut_single);
+									cvCopy(detect, img_single_result);
+									cvResetImageROI(detect);
+
+									if (showSteps)
+									{
+										char windowsname[200];
+										sprintf(windowsname, "%s%d", "detect", i);//�ֱ���ʾ�ָ�ͼ��
+										cvNamedWindow(windowsname, 1);
+										cvShowImage(windowsname, img_single_result);
+									}
+
+									IplImage* img_gray = cvCreateImage(cvGetSize(img_single_result), IPL_DEPTH_8U, 1);//����Ŀ��ͼ��  
+									cvCvtColor(img_single_result, img_gray, CV_BGR2GRAY);
+									cv::Mat img_re = cv::cvarrToMat(img_gray);
+									/*IplImage* img_re = cvCreateImage(cvSize(charRec.getWidth(), charRec.getHeight()), 8, charRec.getChannel());
+									cvResize(img_gray, img_re);*/
+									int predict = -1;
+									double loss = 0.0;
+
+									if (i == 0) {
+
+										rec_char(net_chinese, img_re, predict, loss);
+										/*charRec0.recognise_form_memory_scale0_2(img_re, &predict, &loss);*/
+
+										if (predict > 30) {
+											predict = -1;
+										}
+
+										char* hanzi = outputhanzi(predict);
+
+										Pred pred;
+										pred.first = cvRect(ROI_choose[i].x + face.x, ROI_choose[i].y + face.y, ROI_choose[i].width, ROI_choose[i].height);
+										pred.second = hanzi;
+										result.one_char.push_back(pred);
+
+										if (showSteps)
+											printf("����-loss=%f \n", loss);
+
+									}
+									else {
+										rec_char(net_char, img_re, predict, loss);
+										/*if (predict == -1)
+										predict = 0;*/
+										/*charRec.recognise_form_memory_scale0_1(img_re, &predict, &loss);*/
+
+										if ((i == 1) && (predict == 8))
+											predict = 11;
+										char*  szzm = outputplate(predict);
+										Pred pred;
+										pred.first = cvRect(ROI_choose[i].x + face.x, ROI_choose[i].y + face.y, ROI_choose[i].width, ROI_choose[i].height);
+										pred.second = szzm;
+										result.one_char.push_back(pred);
+
+
+										if (showSteps)
+											printf("predict=%d loss=%f \n", predict, loss);
+									}
+
+
+
+									/*if (loss < 0.6)
+									predict = -1;*/
+								}
+							}
+						}
+					}
+
+
+					/*if (showSteps)
+					{
+						cvDestroyAllWindows();
+					}*/
+
+				}
+
+				results.push_back(result);
+				//#########################################################
+
+
+#endif // HAAR_CASCADE
+
+		
+
+
+			}
+
+			
+
+			if (results.size() == NULL) {
+				continue;
+			}
+
+
+			IplImage *showcarplate;
+			if (faceInfo.size() == 0)
+				showcarplate = &IplImage(image);
+			else
+				showcarplate = &IplImage(warp_dstImage);
+			for (int num = 0; num < (int)results.size(); num++)
+			{
+				CvRect face = results[num].coordinate;
+				cvRectangle(showcarplate, cvPoint(face.x, face.y), cvPoint(face.x + face.width, face.y + face.height), CV_RGB(255, 255, 0), 3, 4, 0);
+				vector<Pred> carplate = results[num].one_char;
+				///***************************  ������ʾ���  *****************************///
+				if (carplate.size() >= 7 && showdemo)
+				{
+				//	CvxText text("simhei.ttf");
+					char *strID = new char(100); strID[0] = '\0';
+					char *strID_CN = new char(100); strID_CN[0] = '\0';
+
+					strcat(strID_CN, carplate[0].second);
+
+					for (int i = 1; i < carplate.size(); i++)
+					{
+						strcat(strID, carplate[i].second);
+					}
+
+
+					CvScalar color;
+					color.val[0] = face.height *0.3;
+					color.val[1] = 0.5;
+					color.val[2] = 0.1;
+					color.val[3] = 0;
+				//	text.restoreFont();
+
+				//	text.setFont(NULL, &color, NULL, NULL);
+					if (faceInfo.size() == 0) {
+						cvRectangle(showcarplate, cvPoint(face.x - face.height*0.1, face.y - 5), cvPoint(face.x + face.height * 1.2, face.y - 10 - face.height*0.3), CV_RGB(0, 0, 0), -1, 4, 0);
+						for (int i = 0; i < carplate.size(); i++)
+						{
+							cvRectangle(showcarplate, cvPoint(carplate[i].first.x, carplate[i].first.y), cvPoint(carplate[i].first.x + carplate[i].first.width, carplate[i].first.y + carplate[i].first.height), CV_RGB(0, 255, 0), 1, 4, 0);
+						}
+						//	text.putText(showcarplate, strID, cvPoint(face.x + face.height *0.3, face.y - 10), CV_RGB(255, 255, 255));
+						//	text.putText(showcarplate, strID_CN, cvPoint(face.x, face.y - 10), CV_RGB(255, 255, 255));
+					}
+					else if(faceInfo.size() != 0) {
+						
+						
+						/*cvRectangle(showcarplate, cvPoint(face.x - face.height*0.1, face.y - 5), cvPoint(face.x + face.height * 1.2, face.y - 10 - face.height*0.3), CV_RGB(0, 0, 0), -1, 4, 0);*/
+						for (int i = 0; i < carplate.size(); i++)
+						{
+							carplate[i].first.x -= face.x; carplate[i].first.y -= face.y;
+							cvRectangle(showcarplate, cvPoint(carplate[i].first.x, carplate[i].first.y), cvPoint(carplate[i].first.x + carplate[i].first.width, carplate[i].first.y + carplate[i].first.height), CV_RGB(0, 255, 0), 1, 4, 0);
+						}
+					}
+					for (int i = 0; i < carplate.size(); i++)
+					{
+						printf("%s", carplate[i].second);
+					}
+					cvNamedWindow("showcarplate", 0);
+					cvShowImage("showcarplate", showcarplate);
+
+					carplate.clear();
+					printf(" \n");
+				}
+
+
+
+
+				if (0)
+				{
+					int c = cvWaitKey();
+					if ((c == 's') || (c == 'S')) {
+						_mkdir("error");
+
+						SYSTEMTIME stTime;
+						GetLocalTime(&stTime);
+						char pVideoName[256];
+						sprintf_s(pVideoName, 256, "\\%d_%d_%d_%d_%d_%d_%d", stTime.wYear, stTime.wMonth, stTime.wDay, stTime.wHour, stTime.wMinute, stTime.wSecond, stTime.wMilliseconds);
+						char image_name1[500];
+						sprintf_s(image_name1, 500, "%s%s%s%s", "error", "\\", pVideoName, ".bmp");//�����ͼƬ��				
+						imwrite(image_name1, image);
+
+
+						_mkdir("orig_result");
+						char outName[200];
+						sprintf_s(outName, 200, "%s%s%s", "orig_result", "\\", file_name[iNum].c_str());
+						cout << iNum << outName << endl;
+						MoveFile(img_path[iNum].c_str(), outName);    // ��D��2.txt�ƶ���E�� 
+
+
+					}
+					else if ((c == 'b') || (c == 'B')) {
+						if (iNum > 1) {
+							iNum--; iNum--;
+						}
+					}
+				}
+
+
+
+			}
+
+
+
+
+
+
+			cv::namedWindow("Extracted frame_", 0);
+			cv::imshow("Extracted frame_", image);
+
+			int c = cv::waitKey(0);
+			//����ESC���ߵ���ָ���Ľ���֡���˳���ȡ��Ƶ  
+			if ((char)c == 27 /*|| currentFrame > frameToStop*/)
+			{
+				stop = true;
+			}
+			//���°������ͣ���ڵ�ǰ֡���ȴ���һ�ΰ���  
+			//if (c >= 0)
+			//{
+			//	waitKey(0);
+			//}
+			//	currentFrame++;
+
+		}
+
+		//�ر���Ƶ�ļ�  
+		capture.release();
+	}
+	return 0;
+
+
+}
\ No newline at end of file
diff --git a/ocr_test/ocr_test.vcxproj b/ocr_test/ocr_test.vcxproj
new file mode 100644
index 0000000000000000000000000000000000000000..7964189feef0310474354a00e8dc22afac803d71
--- /dev/null
+++ b/ocr_test/ocr_test.vcxproj
@@ -0,0 +1,184 @@
+<?xml version="1.0" encoding="utf-8"?>
+<Project DefaultTargets="Build" ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+  <ItemGroup Label="ProjectConfigurations">
+    <ProjectConfiguration Include="Debug|Win32">
+      <Configuration>Debug</Configuration>
+      <Platform>Win32</Platform>
+    </ProjectConfiguration>
+    <ProjectConfiguration Include="Release|Win32">
+      <Configuration>Release</Configuration>
+      <Platform>Win32</Platform>
+    </ProjectConfiguration>
+    <ProjectConfiguration Include="Debug|x64">
+      <Configuration>Debug</Configuration>
+      <Platform>x64</Platform>
+    </ProjectConfiguration>
+    <ProjectConfiguration Include="Release|x64">
+      <Configuration>Release</Configuration>
+      <Platform>x64</Platform>
+    </ProjectConfiguration>
+  </ItemGroup>
+  <PropertyGroup Label="Globals">
+    <ProjectGuid>{2CC545B1-643B-4307-9D90-13E93E8FEBD3}</ProjectGuid>
+    <Keyword>Win32Proj</Keyword>
+    <RootNamespace>ocr_test</RootNamespace>
+    <WindowsTargetPlatformVersion>8.1</WindowsTargetPlatformVersion>
+  </PropertyGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>true</UseDebugLibraries>
+    <PlatformToolset>v140</PlatformToolset>
+    <CharacterSet>Unicode</CharacterSet>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>false</UseDebugLibraries>
+    <PlatformToolset>v140</PlatformToolset>
+    <WholeProgramOptimization>true</WholeProgramOptimization>
+    <CharacterSet>Unicode</CharacterSet>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>true</UseDebugLibraries>
+    <PlatformToolset>v140</PlatformToolset>
+    <CharacterSet>MultiByte</CharacterSet>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>false</UseDebugLibraries>
+    <PlatformToolset>v140</PlatformToolset>
+    <WholeProgramOptimization>true</WholeProgramOptimization>
+    <CharacterSet>MultiByte</CharacterSet>
+  </PropertyGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
+  <ImportGroup Label="ExtensionSettings">
+    <Import Project="$(VCTargetsPath)\BuildCustomizations\CUDA 9.2.props" />
+  </ImportGroup>
+  <ImportGroup Label="Shared">
+  </ImportGroup>
+  <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
+  </ImportGroup>
+  <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
+  </ImportGroup>
+  <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
+    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
+  </ImportGroup>
+  <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
+    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
+  </ImportGroup>
+  <PropertyGroup Label="UserMacros" />
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <LinkIncremental>true</LinkIncremental>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
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+
+
+layer {
+  name: "data"
+  type: "ImageData"
+  #type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_value:152
+    mean_value:152
+    mean_value:152
+  }
+  image_data_param {
+  #data_param {
+    source: "C:\\plate_card_BLSTM\\train_tools\\lstm_plate_char_label_train.txt"
+	new_height: 32
+	new_width: 280
+	is_color: 1
+	#root_folder: "C:/plate_card_BLSTM/train_data/train/"
+    batch_size: 96
+	#rand_skip: 1968
+	shuffle: true
+  }
+}
+
+layer {
+  name: "data"
+  type: "ImageData"
+  #type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_value:152
+    mean_value:152
+    mean_value:152
+  }
+  image_data_param {
+  #data_param {
+    source: "C:\\plate_card_BLSTM\\train_tools\\lstm_plate_char_label_test.txt"
+	new_height: 32
+	new_width: 280
+	is_color: 1
+	#root_folder: "C:\\plate_card_BLSTM\\train_data\\train\\"
+    batch_size: 96
+	shuffle: true
+	#rand_skip: 1968
+  }
+}
+
+layer {
+  bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+	param {
+		lr_mult: 1
+		decay_mult: 1
+    }
+    convolution_param {
+    num_output: 64
+      kernel_size: 5
+      pad: 2
+      stride: 2
+	  weight_filler { type: "msra"}
+	  bias_filler { type: "constant" value: 0 }
+      }
+}
+
+
+# DenseBlock 1
+layer {
+  name: "DenseBlock1"
+  type: "DenseBlock"
+  bottom: "conv1"
+  top: "DenseBlock1"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm1"
+  type: "BatchNorm"
+  bottom: "DenseBlock1"
+  top: "BatchNorm1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "BatchNorm1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout1"
+  type: "Dropout"
+  bottom: "Convolution2"
+  top: "Dropout1"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Dropout1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 2
+layer {
+  name: "DenseBlock2"
+  type: "DenseBlock"
+  bottom: "Pooling1"
+  top: "DenseBlock2"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm2"
+  type: "BatchNorm"
+  bottom: "DenseBlock2"
+  top: "BatchNorm2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+}
+
+
+
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "BatchNorm2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout2"
+  type: "Dropout"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution3"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 3
+layer {
+  name: "DenseBlock3"
+  type: "DenseBlock"
+  bottom: "Pooling2"
+  top: "DenseBlock3"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm3"
+  type: "BatchNorm"
+  bottom: "DenseBlock3"
+  top: "BatchNorm3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+}
+
+
+layer {
+  name: "pool5_ave"
+  type: "Pooling"
+  bottom: "BatchNorm3"
+  top: "pool5_ave"
+  pooling_param {
+    pool: AVE
+    kernel_w: 1
+	kernel_h: 4
+    stride_w: 1
+	stride_h: 1
+  }
+}
+
+layer {
+  name: "pool5_ave_transpose"
+  top: "pool5_ave_transpose"
+  bottom: "pool5_ave"
+  type: "Transpose"
+  transpose_param {
+    dim: 3
+    dim: 2
+    dim: 0
+    dim: 1
+  }
+}
+
+layer {
+  name: "blstm_input"
+  type: "Reshape"
+  bottom: "pool5_ave_transpose"
+  top: "blstm_input"
+  reshape_param {
+    shape { dim: -1 }
+    axis: 1
+    num_axes: 2
+  }
+}
+
+#===================blstm layer 1============================
+#======lstm1===================
+layer {
+  name: "lstm1"
+  type: "Lstm"
+  bottom: "blstm_input"
+  top: "lstm1"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm1_reverse===================
+layer {
+  name: "lstm1-reverse1"
+  type: "Reverse"
+  bottom: "blstm_input"
+  top: "rlstm1_input"
+  reverse_param {
+    axis: 0
+  }
+}
+layer {
+  name: "rlstm1"
+  type: "Lstm"
+  bottom: "rlstm1_input"
+  top: "rlstm1-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm1-reverse2"
+  type: "Reverse"
+  bottom: "rlstm1-output"
+  top: "rlstm1"
+  reverse_param {
+    axis: 0
+  }
+}
+
+
+# merge lstm1 and rlstm1
+layer {
+  name: "blstm1"
+  type: "Eltwise"
+  bottom: "lstm1"
+  bottom: "rlstm1"
+  bottom: "blstm_input"
+  top: "blstm1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+
+ 
+
+#===================blstm layer 2============================
+#======lstm2===================
+layer {
+  name: "lstm2"
+  type: "Lstm"
+  bottom: "blstm1"
+  top: "lstm2"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm2_reverse===================
+layer {
+  name: "lstm2-reverse1"
+  type: "Reverse"
+  bottom: "blstm1"
+  top: "rlstm2_input"
+  reverse_param {
+    axis: 0
+  }
+}
+
+layer {
+  name: "rlstm2"
+  type: "Lstm"
+  bottom: "rlstm2_input"
+  top: "rlstm2-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm2-reverse2"
+  type: "Reverse"
+  bottom: "rlstm2-output"
+  top: "rlstm2"
+  reverse_param {
+    axis: 0
+  }
+}
+
+# merge lstm2 and rlstm2
+layer {
+  name: "blstm2"
+  type: "Eltwise"
+  bottom: "lstm2"
+  bottom: "rlstm2"
+  bottom: "blstm1"
+  bottom: "blstm_input"
+  top: "blstm2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+layer {
+  name: "fc1x"
+  type: "InnerProduct"
+  bottom: "blstm2"
+  top: "fc1x"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    axis: 2
+    num_output: 69
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+  name: "ctcloss"
+  type: "WarpCTCLoss"
+  bottom: "fc1x"
+  bottom: "label"
+  top: "ctcloss"
+  loss_weight:1
+}
+
+layer {
+	name: "acc"
+	type: "CTCGreedyDecoder"
+	bottom: "fc1x"
+	bottom: "label"
+	top: "acc"
+	include {
+    phase: TEST
+  }
+}
\ No newline at end of file
diff --git a/ocr_test/plateCard_test/deploy.dll b/ocr_test/plateCard_test/deploy.dll
new file mode 100644
index 0000000000000000000000000000000000000000..9a90f1d2216b6cf8b8b5a78af1ee82068172ee75
--- /dev/null
+++ b/ocr_test/plateCard_test/deploy.dll
@@ -0,0 +1,514 @@
+name: "densenet"
+
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 32
+input_dim: 280
+
+
+layer {
+  bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+	param {
+		lr_mult: 1
+		decay_mult: 1
+    }
+    convolution_param {
+    num_output: 64
+      kernel_size: 5
+      pad: 2
+      stride: 2
+	  weight_filler { type: "msra"}
+	  bias_filler { type: "constant" value: 0 }
+      }
+}
+
+
+# DenseBlock 1
+layer {
+  name: "DenseBlock1"
+  type: "DenseBlock"
+  bottom: "conv1"
+  top: "DenseBlock1"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm1"
+  type: "BatchNorm"
+  bottom: "DenseBlock1"
+  top: "BatchNorm1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "BatchNorm1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout1"
+  type: "Dropout"
+  bottom: "Convolution2"
+  top: "Dropout1"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Dropout1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 2
+layer {
+  name: "DenseBlock2"
+  type: "DenseBlock"
+  bottom: "Pooling1"
+  top: "DenseBlock2"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm2"
+  type: "BatchNorm"
+  bottom: "DenseBlock2"
+  top: "BatchNorm2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+}
+
+
+
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "BatchNorm2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout2"
+  type: "Dropout"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution3"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 3
+layer {
+  name: "DenseBlock3"
+  type: "DenseBlock"
+  bottom: "Pooling2"
+  top: "DenseBlock3"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm3"
+  type: "BatchNorm"
+  bottom: "DenseBlock3"
+  top: "BatchNorm3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+}
+
+
+layer {
+  name: "pool5_ave"
+  type: "Pooling"
+  bottom: "BatchNorm3"
+  top: "pool5_ave"
+  pooling_param {
+    pool: AVE
+    kernel_w: 1
+	kernel_h: 4
+    stride_w: 1
+	stride_h: 1
+  }
+}
+
+layer {
+  name: "pool5_ave_transpose"
+  top: "pool5_ave_transpose"
+  bottom: "pool5_ave"
+  type: "Transpose"
+  transpose_param {
+    dim: 3
+    dim: 2
+    dim: 0
+    dim: 1
+  }
+}
+
+layer {
+  name: "blstm_input"
+  type: "Reshape"
+  bottom: "pool5_ave_transpose"
+  top: "blstm_input"
+  reshape_param {
+    shape { dim: -1 }
+    axis: 1
+    num_axes: 2
+  }
+}
+
+#===================blstm layer 1============================
+#======lstm1===================
+layer {
+  name: "lstm1"
+  type: "Lstm"
+  bottom: "blstm_input"
+  top: "lstm1"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm1_reverse===================
+layer {
+  name: "lstm1-reverse1"
+  type: "Reverse"
+  bottom: "blstm_input"
+  top: "rlstm1_input"
+  reverse_param {
+    axis: 0
+  }
+}
+layer {
+  name: "rlstm1"
+  type: "Lstm"
+  bottom: "rlstm1_input"
+  top: "rlstm1-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm1-reverse2"
+  type: "Reverse"
+  bottom: "rlstm1-output"
+  top: "rlstm1"
+  reverse_param {
+    axis: 0
+  }
+}
+
+
+# merge lstm1 and rlstm1
+layer {
+  name: "blstm1"
+  type: "Eltwise"
+  bottom: "lstm1"
+  bottom: "rlstm1"
+  bottom: "blstm_input"
+  top: "blstm1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+
+ 
+
+#===================blstm layer 2============================
+#======lstm2===================
+layer {
+  name: "lstm2"
+  type: "Lstm"
+  bottom: "blstm1"
+  top: "lstm2"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm2_reverse===================
+layer {
+  name: "lstm2-reverse1"
+  type: "Reverse"
+  bottom: "blstm1"
+  top: "rlstm2_input"
+  reverse_param {
+    axis: 0
+  }
+}
+
+layer {
+  name: "rlstm2"
+  type: "Lstm"
+  bottom: "rlstm2_input"
+  top: "rlstm2-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm2-reverse2"
+  type: "Reverse"
+  bottom: "rlstm2-output"
+  top: "rlstm2"
+  reverse_param {
+    axis: 0
+  }
+}
+
+# merge lstm2 and rlstm2
+layer {
+  name: "blstm2"
+  type: "Eltwise"
+  bottom: "lstm2"
+  bottom: "rlstm2"
+  bottom: "blstm1"
+  bottom: "blstm_input"
+  top: "blstm2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+layer {
+  name: "fc1x"
+  type: "InnerProduct"
+  bottom: "blstm2"
+  top: "fc1x"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    axis: 2
+    num_output: 69
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+	name: "result"
+	type: "CTCGreedyDecoder"
+	bottom: "fc1x"
+	top: "result"
+}
\ No newline at end of file
diff --git a/ocr_test/plateCard_test/label.dll b/ocr_test/plateCard_test/label.dll
new file mode 100644
index 0000000000000000000000000000000000000000..4020e2aa4b1022068ee32177fc2a935f48e6e7a9
--- /dev/null
+++ b/ocr_test/plateCard_test/label.dll
@@ -0,0 +1,69 @@
+blank
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+A
+B
+C
+D
+E
+F
+G
+H
+J
+K
+L
+M
+N
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+ѧ
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+³
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+��
+ԥ
+��
+��
+��
\ No newline at end of file
diff --git a/ocr_test/plateCard_test/mean_values.txt b/ocr_test/plateCard_test/mean_values.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f2c94c6579fc063bea87dec00bad89680fcf6473
--- /dev/null
+++ b/ocr_test/plateCard_test/mean_values.txt
@@ -0,0 +1,3 @@
+152
+152
+152
\ No newline at end of file
diff --git a/ocr_test/plateCard_test/model.dll b/ocr_test/plateCard_test/model.dll
new file mode 100644
index 0000000000000000000000000000000000000000..86368bfc3154ef27b2af42a259292321dd1d900c
Binary files /dev/null and b/ocr_test/plateCard_test/model.dll differ
diff --git a/ocr_test/plateCard_test/model46.caffemodel b/ocr_test/plateCard_test/model46.caffemodel
new file mode 100644
index 0000000000000000000000000000000000000000..6fb9e6c0950a22d61b44de43111e5e4586bced0a
Binary files /dev/null and b/ocr_test/plateCard_test/model46.caffemodel differ
diff --git "a/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_420000.caffemodel" "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_420000.caffemodel"
new file mode 100644
index 0000000000000000000000000000000000000000..9c5b7c489ac1a12a8ebd58f1b5e32a5bbb51c9be
Binary files /dev/null and "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_420000.caffemodel" differ
diff --git "a/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_420000.solverstate" "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_420000.solverstate"
new file mode 100644
index 0000000000000000000000000000000000000000..ba091b87a3cb482c12d9493e28e29be84ebc5d48
Binary files /dev/null and "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_420000.solverstate" differ
diff --git "a/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_682957.solverstate" "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_682957.solverstate"
new file mode 100644
index 0000000000000000000000000000000000000000..d014c8d451acb2ed938f48990c819ad719c1a7ad
Binary files /dev/null and "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-bigger-5x5-no-lstm_iter_682957.solverstate" differ
diff --git "a/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-sum-blstm-full-res-blstm_train-val.prototxt" "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-sum-blstm-full-res-blstm_train-val.prototxt"
new file mode 100644
index 0000000000000000000000000000000000000000..f1bb8eec26728bbc2c62e9c0d9030a86e2aac5a8
--- /dev/null
+++ "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/densenet-sum-blstm-full-res-blstm_train-val.prototxt"
@@ -0,0 +1,574 @@
+
+
+layer {
+  name: "data"
+  type: "ImageData"
+  #type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_value:152
+    mean_value:152
+    mean_value:152
+  }
+  image_data_param {
+  #data_param {
+    source: "lstm_plate_char_label_train.txt"
+	new_height: 32
+	new_width: 280
+	is_color: 1
+	root_folder: "C:\\plate_card_BLSTM\\train\\"
+    batch_size: 96
+	#rand_skip: 1968
+	shuffle: true
+  }
+}
+
+layer {
+  name: "data"
+  type: "ImageData"
+  #type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_value:152
+    mean_value:152
+    mean_value:152
+  }
+  image_data_param {
+  #data_param {
+    source: "lstm_plate_char_label_test.txt"
+	new_height: 32
+	new_width: 280
+	is_color: 1
+	root_folder: "C:\\plate_card_BLSTM\\train\\"
+    batch_size: 64
+	shuffle: true
+	#rand_skip: 1968
+  }
+}
+
+layer {
+  bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+	param {
+		lr_mult: 1
+		decay_mult: 1
+    }
+    convolution_param {
+    num_output: 64
+      kernel_size: 5
+      pad: 2
+      stride: 2
+	  weight_filler { type: "msra"}
+	  bias_filler { type: "constant" value: 0 }
+      }
+}
+
+
+# DenseBlock 1
+layer {
+  name: "DenseBlock1"
+  type: "DenseBlock"
+  bottom: "conv1"
+  top: "DenseBlock1"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm1"
+  type: "BatchNorm"
+  bottom: "DenseBlock1"
+  top: "BatchNorm1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "BatchNorm1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout1"
+  type: "Dropout"
+  bottom: "Convolution2"
+  top: "Dropout1"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Dropout1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 2
+layer {
+  name: "DenseBlock2"
+  type: "DenseBlock"
+  bottom: "Pooling1"
+  top: "DenseBlock2"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm2"
+  type: "BatchNorm"
+  bottom: "DenseBlock2"
+  top: "BatchNorm2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+}
+
+
+
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "BatchNorm2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout2"
+  type: "Dropout"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution3"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 3
+layer {
+  name: "DenseBlock3"
+  type: "DenseBlock"
+  bottom: "Pooling2"
+  top: "DenseBlock3"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm3"
+  type: "BatchNorm"
+  bottom: "DenseBlock3"
+  top: "BatchNorm3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+}
+
+
+layer {
+  name: "pool5_ave"
+  type: "Pooling"
+  bottom: "BatchNorm3"
+  top: "pool5_ave"
+  pooling_param {
+    pool: AVE
+    kernel_w: 1
+	kernel_h: 4
+    stride_w: 1
+	stride_h: 1
+  }
+}
+
+layer {
+  name: "pool5_ave_transpose"
+  top: "pool5_ave_transpose"
+  bottom: "pool5_ave"
+  type: "Transpose"
+  transpose_param {
+    dim: 3
+    dim: 2
+    dim: 0
+    dim: 1
+  }
+}
+
+layer {
+  name: "blstm_input"
+  type: "Reshape"
+  bottom: "pool5_ave_transpose"
+  top: "blstm_input"
+  reshape_param {
+    shape { dim: -1 }
+    axis: 1
+    num_axes: 2
+  }
+}
+
+#===================blstm layer 1============================
+#======lstm1===================
+layer {
+  name: "lstm1"
+  type: "Lstm"
+  bottom: "blstm_input"
+  top: "lstm1"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm1_reverse===================
+layer {
+  name: "lstm1-reverse1"
+  type: "Reverse"
+  bottom: "blstm_input"
+  top: "rlstm1_input"
+  reverse_param {
+    axis: 0
+  }
+}
+layer {
+  name: "rlstm1"
+  type: "Lstm"
+  bottom: "rlstm1_input"
+  top: "rlstm1-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm1-reverse2"
+  type: "Reverse"
+  bottom: "rlstm1-output"
+  top: "rlstm1"
+  reverse_param {
+    axis: 0
+  }
+}
+
+
+# merge lstm1 and rlstm1
+layer {
+  name: "blstm1"
+  type: "Eltwise"
+  bottom: "lstm1"
+  bottom: "rlstm1"
+  bottom: "blstm_input"
+  top: "blstm1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+
+ 
+
+#===================blstm layer 2============================
+#======lstm2===================
+layer {
+  name: "lstm2"
+  type: "Lstm"
+  bottom: "blstm1"
+  top: "lstm2"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm2_reverse===================
+layer {
+  name: "lstm2-reverse1"
+  type: "Reverse"
+  bottom: "blstm1"
+  top: "rlstm2_input"
+  reverse_param {
+    axis: 0
+  }
+}
+
+layer {
+  name: "rlstm2"
+  type: "Lstm"
+  bottom: "rlstm2_input"
+  top: "rlstm2-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm2-reverse2"
+  type: "Reverse"
+  bottom: "rlstm2-output"
+  top: "rlstm2"
+  reverse_param {
+    axis: 0
+  }
+}
+
+# merge lstm2 and rlstm2
+layer {
+  name: "blstm2"
+  type: "Eltwise"
+  bottom: "lstm2"
+  bottom: "rlstm2"
+  bottom: "blstm1"
+  bottom: "blstm_input"
+  top: "blstm2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+layer {
+  name: "fc1x_69"
+  type: "InnerProduct"
+  bottom: "blstm2"
+  top: "fc1x"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    axis: 2
+    num_output: 69
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+  name: "ctcloss"
+  type: "WarpCTCLoss"
+  bottom: "fc1x"
+  bottom: "label"
+  top: "ctcloss"
+  loss_weight:1
+}
+
+layer {
+	name: "acc"
+	type: "CTCGreedyDecoder"
+	bottom: "fc1x"
+	bottom: "label"
+	top: "acc"
+	include {
+    phase: TEST
+  }
+}
\ No newline at end of file
diff --git "a/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/deploy.prototxt" "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/deploy.prototxt"
new file mode 100644
index 0000000000000000000000000000000000000000..4c39f05f04b83bdbe8bd8d29d048b656ce982f85
--- /dev/null
+++ "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/deploy.prototxt"
@@ -0,0 +1,514 @@
+name: "densenet"
+
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 32
+input_dim: 280
+
+
+layer {
+  bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+	param {
+		lr_mult: 1
+		decay_mult: 1
+    }
+    convolution_param {
+    num_output: 64
+      kernel_size: 5
+      pad: 2
+      stride: 2
+	  weight_filler { type: "msra"}
+	  bias_filler { type: "constant" value: 0 }
+      }
+}
+
+
+# DenseBlock 1
+layer {
+  name: "DenseBlock1"
+  type: "DenseBlock"
+  bottom: "conv1"
+  top: "DenseBlock1"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm1"
+  type: "BatchNorm"
+  bottom: "DenseBlock1"
+  top: "BatchNorm1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "BatchNorm1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout1"
+  type: "Dropout"
+  bottom: "Convolution2"
+  top: "Dropout1"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Dropout1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 2
+layer {
+  name: "DenseBlock2"
+  type: "DenseBlock"
+  bottom: "Pooling1"
+  top: "DenseBlock2"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm2"
+  type: "BatchNorm"
+  bottom: "DenseBlock2"
+  top: "BatchNorm2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+}
+
+
+
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "BatchNorm2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout2"
+  type: "Dropout"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution3"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 3
+layer {
+  name: "DenseBlock3"
+  type: "DenseBlock"
+  bottom: "Pooling2"
+  top: "DenseBlock3"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm3"
+  type: "BatchNorm"
+  bottom: "DenseBlock3"
+  top: "BatchNorm3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+}
+
+
+layer {
+  name: "pool5_ave"
+  type: "Pooling"
+  bottom: "BatchNorm3"
+  top: "pool5_ave"
+  pooling_param {
+    pool: AVE
+    kernel_w: 1
+	kernel_h: 4
+    stride_w: 1
+	stride_h: 1
+  }
+}
+
+layer {
+  name: "pool5_ave_transpose"
+  top: "pool5_ave_transpose"
+  bottom: "pool5_ave"
+  type: "Transpose"
+  transpose_param {
+    dim: 3
+    dim: 2
+    dim: 0
+    dim: 1
+  }
+}
+
+layer {
+  name: "blstm_input"
+  type: "Reshape"
+  bottom: "pool5_ave_transpose"
+  top: "blstm_input"
+  reshape_param {
+    shape { dim: -1 }
+    axis: 1
+    num_axes: 2
+  }
+}
+
+#===================blstm layer 1============================
+#======lstm1===================
+layer {
+  name: "lstm1"
+  type: "Lstm"
+  bottom: "blstm_input"
+  top: "lstm1"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm1_reverse===================
+layer {
+  name: "lstm1-reverse1"
+  type: "Reverse"
+  bottom: "blstm_input"
+  top: "rlstm1_input"
+  reverse_param {
+    axis: 0
+  }
+}
+layer {
+  name: "rlstm1"
+  type: "Lstm"
+  bottom: "rlstm1_input"
+  top: "rlstm1-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm1-reverse2"
+  type: "Reverse"
+  bottom: "rlstm1-output"
+  top: "rlstm1"
+  reverse_param {
+    axis: 0
+  }
+}
+
+
+# merge lstm1 and rlstm1
+layer {
+  name: "blstm1"
+  type: "Eltwise"
+  bottom: "lstm1"
+  bottom: "rlstm1"
+  bottom: "blstm_input"
+  top: "blstm1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+
+ 
+
+#===================blstm layer 2============================
+#======lstm2===================
+layer {
+  name: "lstm2"
+  type: "Lstm"
+  bottom: "blstm1"
+  top: "lstm2"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm2_reverse===================
+layer {
+  name: "lstm2-reverse1"
+  type: "Reverse"
+  bottom: "blstm1"
+  top: "rlstm2_input"
+  reverse_param {
+    axis: 0
+  }
+}
+
+layer {
+  name: "rlstm2"
+  type: "Lstm"
+  bottom: "rlstm2_input"
+  top: "rlstm2-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm2-reverse2"
+  type: "Reverse"
+  bottom: "rlstm2-output"
+  top: "rlstm2"
+  reverse_param {
+    axis: 0
+  }
+}
+
+# merge lstm2 and rlstm2
+layer {
+  name: "blstm2"
+  type: "Eltwise"
+  bottom: "lstm2"
+  bottom: "rlstm2"
+  bottom: "blstm1"
+  bottom: "blstm_input"
+  top: "blstm2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+layer {
+  name: "fc1x_69"
+  type: "InnerProduct"
+  bottom: "blstm2"
+  top: "fc1x"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    axis: 2
+    num_output: 69
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+	name: "result"
+	type: "CTCGreedyDecoder"
+	bottom: "fc1x"
+	top: "result"
+}
\ No newline at end of file
diff --git "a/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/model.caffemodel" "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/model.caffemodel"
new file mode 100644
index 0000000000000000000000000000000000000000..3e54f97982efe3b56f42a1cec0f7bd86df3f228d
Binary files /dev/null and "b/ocr_test/plateCard_test/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/model.caffemodel" differ
diff --git a/ocr_test/public.h b/ocr_test/public.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a5bfcf0594d90a504435d86dc5abb9a824d6303
--- /dev/null
+++ b/ocr_test/public.h
@@ -0,0 +1,59 @@
+#pragma once
+
+
+//-------------------------------------------------------------------------------------------------
+#ifdef  WIN32
+	#ifndef IMPORT
+	#define IMPORT __declspec(dllimport)
+	#endif
+	#ifndef EXPORT
+	#define EXPORT __declspec(dllexport)
+	#endif
+	#ifndef API
+	#define API __stdcall
+	#endif
+#else
+	#ifndef IMPORT
+	#define IMPORT
+	#endif
+	#ifndef EXPORT
+	#define EXPORT
+	#endif
+	#ifndef API
+	#define API
+	#endif
+#endif
+
+//-------------------------------------------------------------------------------------------------
+#ifndef interface
+#define CINTERFACE
+#define interface struct
+#endif
+
+//-------------------------------------------------------------------------------------------------
+#ifndef min2
+#define min2(a,b)  (((a)<(b)) ? (a) : (b)) 
+#endif
+
+//-------------------------------------------------------------------------------------------------
+#ifndef max2
+#define max2(a,b)  (((a)>(b)) ? (a) : (b))
+#endif
+
+//-------------------------------------------------------------------------------------------------
+#ifndef _rect_
+#define _rect_
+struct rect{ int x0, y0, x1, y1; };
+#define rectw(r) (r.x1-r.x0+1)
+#define recth(r) (r.y1-r.y0+1)
+#endif
+
+//-------------------------------------------------------------------------------------------------
+#ifndef byte
+typedef unsigned char byte;
+#endif
+
+//-------------------------------------------------------------------------------------------------
+#ifndef word
+typedef unsigned short word;
+#endif
diff --git a/ocr_test/rec_test/cascade12.xml b/ocr_test/rec_test/cascade12.xml
new file mode 100644
index 0000000000000000000000000000000000000000..607bd894d731d3ba2569a1413310ae0a13f0572b
--- /dev/null
+++ b/ocr_test/rec_test/cascade12.xml
@@ -0,0 +1,10408 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>25</height>
+  <width>15</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-001</minHitRate>
+    <maxFalseAlarm>3.0000001192092896e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>300</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>11</maxWeakCount>
+      <stageThreshold>-1.6251399517059326e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 694 -7.2086244821548462e-002</internalNodes>
+          <leafValues>
+            6.8792390823364258e-001 -8.1407219171524048e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 138 7.6971486210823059e-002</internalNodes>
+          <leafValues>
+            -4.6762531995773315e-001 7.1302038431167603e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 388 5.7955548167228699e-002</internalNodes>
+          <leafValues>
+            -2.7737632393836975e-001 8.8490480184555054e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 313 -1.7470759153366089e-001</internalNodes>
+          <leafValues>
+            7.7989900112152100e-001 -2.0272587239742279e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 570 -3.0315775424242020e-002</internalNodes>
+          <leafValues>
+            7.3579686880111694e-001 -2.9271504282951355e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 466 -2.2699251770973206e-001</internalNodes>
+          <leafValues>
+            -8.7592130899429321e-001 2.6184245944023132e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 472 2.2222604602575302e-002</internalNodes>
+          <leafValues>
+            -2.3584529757499695e-001 5.2793049812316895e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 238 -1.5160607174038887e-002</internalNodes>
+          <leafValues>
+            6.6588300466537476e-001 -2.1023984253406525e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 293 2.2018256783485413e-001</internalNodes>
+          <leafValues>
+            -1.4480605721473694e-001 7.7717304229736328e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 774 4.9290269613265991e-002</internalNodes>
+          <leafValues>
+            1.8850675225257874e-001 -7.3545551300048828e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 610 -5.6491583585739136e-002</internalNodes>
+          <leafValues>
+            5.9895467758178711e-001 -1.9187590479850769e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>21</maxWeakCount>
+      <stageThreshold>-1.9631267786026001e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 272 4.8581756651401520e-002</internalNodes>
+          <leafValues>
+            -8.0708128213882446e-001 3.7584605813026428e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 400 -2.9397435486316681e-002</internalNodes>
+          <leafValues>
+            6.1153280735015869e-001 -4.8089566826820374e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 314 -3.1789845228195190e-001</internalNodes>
+          <leafValues>
+            7.8902244567871094e-001 -2.0238314568996429e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -1.9626371562480927e-001 6.5736478567123413e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            6.9301313161849976e-001 -1.1920227110385895e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -1.9045864045619965e-001 5.1003587245941162e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -1.9191724061965942e-001 5.1996380090713501e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            8.0145609378814697e-001 -1.0254951566457748e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            6.9960737228393555e-001 -1.0233960300683975e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -1.6020181775093079e-001 4.8573195934295654e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            1.7845858633518219e-001 -6.3060945272445679e-001</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -6.1882525682449341e-001 1.1507444083690643e-001</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -6.6079556941986084e-001 8.4339156746864319e-002</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 145 -5.2882306277751923e-002</internalNodes>
+          <leafValues>
+            5.0660252571105957e-001 -1.2996160984039307e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 649 -2.3115944117307663e-002</internalNodes>
+          <leafValues>
+            4.4238254427909851e-001 -1.7129151523113251e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 672 2.0139049738645554e-002</internalNodes>
+          <leafValues>
+            -1.0410805791616440e-001 5.5971127748489380e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 799 4.3108966201543808e-003</internalNodes>
+          <leafValues>
+            1.0123755782842636e-001 -6.2685883045196533e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 243 1.4916403219103813e-002</internalNodes>
+          <leafValues>
+            -1.3348171114921570e-001 4.8477530479431152e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>30</maxWeakCount>
+      <stageThreshold>-1.9130507707595825e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -4.6200242638587952e-001 5.0392824411392212e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            4.3452042341232300e-001 -3.0821722745895386e-001</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+    <!-- stage 3 -->
+    <_>
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+      <stageThreshold>-2.0173692703247070e+000</stageThreshold>
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diff --git a/ocr_test/rec_test/char/cifar10_quick_iter_2030578.solverstate b/ocr_test/rec_test/char/cifar10_quick_iter_2030578.solverstate
new file mode 100644
index 0000000000000000000000000000000000000000..8bdbcc291d57b7916fe04ed6e2c0479b3dafe11f
Binary files /dev/null and b/ocr_test/rec_test/char/cifar10_quick_iter_2030578.solverstate differ
diff --git a/ocr_test/rec_test/char/cifar10_quick_train_test.prototxt b/ocr_test/rec_test/char/cifar10_quick_train_test.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..8b06622ae24b30e0afe8fa5472686fc3e131e492
--- /dev/null
+++ b/ocr_test/rec_test/char/cifar10_quick_train_test.prototxt
@@ -0,0 +1,247 @@
+name: "CIFAR10_quick"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  data_param {
+    source: "imagenet_train_leveldb"
+    batch_size: 1024
+    backend: LEVELDB
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  data_param {
+    source: "imagenet_test_leveldb"
+    batch_size: 1024
+    backend: LEVELDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 20
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "bn1"
+  
+  batch_norm_param {
+	  use_global_stats: false
+	}
+}
+
+layer {
+  name: "conv1_scale"
+  type: "Scale"
+  bottom: "bn1"
+  top: "bn1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn1"
+  top: "bn1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "bn1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 50
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  batch_norm_param {
+	  use_global_stats: false
+	}
+}
+
+layer {
+  name: "conv2_scale"
+  type: "Scale"
+  bottom: "bn2"
+  top: "bn2"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "bn2"
+  top: "bn2"
+}
+
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "bn2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn3"
+  type: "BatchNorm"
+  bottom: "ip1"
+  top: "bn3"
+
+  batch_norm_param {
+	  use_global_stats: false
+	}
+}
+layer {
+  name: "ip1_scale"
+  type: "Scale"
+  bottom: "bn3"
+  top: "bn3"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn3"
+  top: "bn3"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "bn3"
+  top: "ip2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 37
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/ocr_test/rec_test/char/model.caffemodel b/ocr_test/rec_test/char/model.caffemodel
new file mode 100644
index 0000000000000000000000000000000000000000..0afcd306645bb87ca5f4cc3bf8b7d1724fad5731
Binary files /dev/null and b/ocr_test/rec_test/char/model.caffemodel differ
diff --git a/ocr_test/rec_test/char/model.prototxt b/ocr_test/rec_test/char/model.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..1392387f52468a79cf6f905ed445bf5209e64b47
--- /dev/null
+++ b/ocr_test/rec_test/char/model.prototxt
@@ -0,0 +1,207 @@
+name: "CIFAR10_quick_test"
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 35
+input_dim: 21
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 20
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "bn1"
+  
+  batch_norm_param {
+	  use_global_stats: true
+	}
+}
+
+layer {
+  name: "conv1_scale"
+  type: "Scale"
+  bottom: "bn1"
+  top: "bn1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn1"
+  top: "bn1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "bn1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 50
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  batch_norm_param {
+	  use_global_stats: true
+	}
+}
+
+layer {
+  name: "conv2_scale"
+  type: "Scale"
+  bottom: "bn2"
+  top: "bn2"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "bn2"
+  top: "bn2"
+}
+
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "bn2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn3"
+  type: "BatchNorm"
+  bottom: "ip1"
+  top: "bn3"
+
+  batch_norm_param {
+	  use_global_stats: true
+	}
+}
+layer {
+  name: "ip1_scale"
+  type: "Scale"
+  bottom: "bn3"
+  top: "bn3"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn3"
+  top: "bn3"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "bn3"
+  top: "ip2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 37
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "ip2"
+  top: "prob"
+}
diff --git a/ocr_test/rec_test/chinese/cifar10_quick_iter_1009313.solverstate b/ocr_test/rec_test/chinese/cifar10_quick_iter_1009313.solverstate
new file mode 100644
index 0000000000000000000000000000000000000000..01fc1fe34522f5544d6f04e0a79a0ead141286f9
Binary files /dev/null and b/ocr_test/rec_test/chinese/cifar10_quick_iter_1009313.solverstate differ
diff --git a/ocr_test/rec_test/chinese/cifar10_quick_train_test.prototxt b/ocr_test/rec_test/chinese/cifar10_quick_train_test.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..2922203815302b8ff90c732067b8a77aa1bcf888
--- /dev/null
+++ b/ocr_test/rec_test/chinese/cifar10_quick_train_test.prototxt
@@ -0,0 +1,247 @@
+name: "CIFAR10_quick"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  data_param {
+    source: "imagenet_train_leveldb"
+    batch_size: 1024
+    backend: LEVELDB
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  data_param {
+    source: "imagenet_test_leveldb"
+    batch_size: 1024
+    backend: LEVELDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 20
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "bn1"
+  
+  batch_norm_param {
+	  use_global_stats: false
+	}
+}
+
+layer {
+  name: "conv1_scale"
+  type: "Scale"
+  bottom: "bn1"
+  top: "bn1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn1"
+  top: "bn1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "bn1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 50
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  batch_norm_param {
+	  use_global_stats: false
+	}
+}
+
+layer {
+  name: "conv2_scale"
+  type: "Scale"
+  bottom: "bn2"
+  top: "bn2"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "bn2"
+  top: "bn2"
+}
+
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "bn2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn3"
+  type: "BatchNorm"
+  bottom: "ip1"
+  top: "bn3"
+
+  batch_norm_param {
+	  use_global_stats: false
+	}
+}
+layer {
+  name: "ip1_scale"
+  type: "Scale"
+  bottom: "bn3"
+  top: "bn3"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn3"
+  top: "bn3"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "bn3"
+  top: "ip2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 31
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/ocr_test/rec_test/chinese/model.caffemodel b/ocr_test/rec_test/chinese/model.caffemodel
new file mode 100644
index 0000000000000000000000000000000000000000..6708222d572523ebb16731e2600dac026280771b
Binary files /dev/null and b/ocr_test/rec_test/chinese/model.caffemodel differ
diff --git a/ocr_test/rec_test/chinese/model.prototxt b/ocr_test/rec_test/chinese/model.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..78b80a16a01f8463dfae84212f6972fe69f6ff21
--- /dev/null
+++ b/ocr_test/rec_test/chinese/model.prototxt
@@ -0,0 +1,207 @@
+name: "CIFAR10_quick_test"
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 35
+input_dim: 21
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 20
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "bn1"
+  
+  batch_norm_param {
+	  use_global_stats: true
+	}
+}
+
+layer {
+  name: "conv1_scale"
+  type: "Scale"
+  bottom: "bn1"
+  top: "bn1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn1"
+  top: "bn1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "bn1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 50
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  batch_norm_param {
+	  use_global_stats: true
+	}
+}
+
+layer {
+  name: "conv2_scale"
+  type: "Scale"
+  bottom: "bn2"
+  top: "bn2"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "bn2"
+  top: "bn2"
+}
+
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "bn2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool2"
+  top: "ip1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "bn3"
+  type: "BatchNorm"
+  bottom: "ip1"
+  top: "bn3"
+
+  batch_norm_param {
+	  use_global_stats: true
+	}
+}
+layer {
+  name: "ip1_scale"
+  type: "Scale"
+  bottom: "bn3"
+  top: "bn3"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn3"
+  top: "bn3"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "bn3"
+  top: "ip2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  inner_product_param {
+    num_output: 31
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "ip2"
+  top: "prob"
+}
diff --git a/ocr_test/stdafx.cpp b/ocr_test/stdafx.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a1008bcfef5c0101afa159668e1b3983a5533f1c
--- /dev/null
+++ b/ocr_test/stdafx.cpp
@@ -0,0 +1,8 @@
+// stdafx.cpp : ֻ������׼�����ļ���Դ�ļ�
+// ocr_test.pch ����ΪԤ����ͷ
+// stdafx.obj ������Ԥ����������Ϣ
+
+#include "stdafx.h"
+
+// TODO: �� STDAFX.H �������κ�����ĸ���ͷ�ļ���
+//�������ڴ��ļ�������
diff --git a/ocr_test/stdafx.h b/ocr_test/stdafx.h
new file mode 100644
index 0000000000000000000000000000000000000000..baa4bbc621f3abfbc1726da79a84e54dbd5309f8
--- /dev/null
+++ b/ocr_test/stdafx.h
@@ -0,0 +1,15 @@
+// stdafx.h : ��׼ϵͳ�����ļ��İ����ļ���
+// ���Ǿ���ʹ�õ��������ĵ�
+// �ض�����Ŀ�İ����ļ�
+//
+
+#pragma once
+
+#include "targetver.h"
+
+#include <stdio.h>
+#include <tchar.h>
+
+
+
+// TODO:  �ڴ˴����ó�����Ҫ������ͷ�ļ�
diff --git a/ocr_test/targetver.h b/ocr_test/targetver.h
new file mode 100644
index 0000000000000000000000000000000000000000..416cebf89fd0df8216693b65bba536a1436754e7
--- /dev/null
+++ b/ocr_test/targetver.h
@@ -0,0 +1,8 @@
+#pragma once
+
+// ���� SDKDDKVer.h ��������õ���߰汾�� Windows ƽ̨��
+
+// ���ҪΪ��ǰ�� Windows ƽ̨����Ӧ�ó�������� WinSDKVer.h������
+// �� _WIN32_WINNT ������ΪҪ֧�ֵ�ƽ̨��Ȼ���ٰ��� SDKDDKVer.h��
+
+#include <SDKDDKVer.h>
diff --git a/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_1303038.caffemodel b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_1303038.caffemodel
new file mode 100644
index 0000000000000000000000000000000000000000..aa4704627999324b1b9d69d0a4c7cef94af4d049
Binary files /dev/null and b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_1303038.caffemodel differ
diff --git a/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_1303038.solverstate b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_1303038.solverstate
new file mode 100644
index 0000000000000000000000000000000000000000..22e1be888a564ca1ea4f9551312dbfd6e8ee4136
Binary files /dev/null and b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_1303038.solverstate differ
diff --git a/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_250043.caffemodel b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_250043.caffemodel
new file mode 100644
index 0000000000000000000000000000000000000000..c80f4ffb5abfc4957d822299b95a9c890e7e89da
Binary files /dev/null and b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_250043.caffemodel differ
diff --git a/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_250043.solverstate b/ocr_test/wm_test/densenet-bigger-5x5-no-lstm_iter_250043.solverstate
new file mode 100644
index 0000000000000000000000000000000000000000..665191f760fedd7dc7c75cb0024d649d3c71f03e
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diff --git a/ocr_test/wm_test/densenet-sum-blstm-full-res-blstm_train-val.prototxt b/ocr_test/wm_test/densenet-sum-blstm-full-res-blstm_train-val.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..ff3b9f1a19f244ec7c8404fa01e4ea87e725e8ce
--- /dev/null
+++ b/ocr_test/wm_test/densenet-sum-blstm-full-res-blstm_train-val.prototxt
@@ -0,0 +1,574 @@
+
+
+layer {
+  name: "data"
+  type: "ImageData"
+  #type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_value:152
+    mean_value:152
+    mean_value:152
+  }
+  image_data_param {
+  #data_param {
+    source: "C:\\WM_LSTM\\1410450762.txt"
+	new_height: 32
+	new_width: 280
+	is_color: 1
+	root_folder: "C:\\WM_LSTM\\train_data1\\"
+    batch_size: 96
+	#rand_skip: 1968
+	shuffle: true
+  }
+}
+
+layer {
+  name: "data"
+  type: "ImageData"
+  #type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_value:152
+    mean_value:152
+    mean_value:152
+  }
+  image_data_param {
+  #data_param {
+    source: "C:\\WM_LSTM\\1410450762_test.txt"
+	new_height: 32
+	new_width: 280
+	is_color: 1
+	root_folder: "C:\\WM_LSTM\\train_data1\\"
+    batch_size: 96
+	shuffle: true
+	#rand_skip: 1968
+  }
+}
+
+layer {
+  bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+	param {
+		lr_mult: 1
+		decay_mult: 1
+    }
+    convolution_param {
+    num_output: 64
+      kernel_size: 5
+      pad: 2
+      stride: 2
+	  weight_filler { type: "msra"}
+	  bias_filler { type: "constant" value: 0 }
+      }
+}
+
+
+# DenseBlock 1
+layer {
+  name: "DenseBlock1"
+  type: "DenseBlock"
+  bottom: "conv1"
+  top: "DenseBlock1"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm1"
+  type: "BatchNorm"
+  bottom: "DenseBlock1"
+  top: "BatchNorm1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "BatchNorm1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout1"
+  type: "Dropout"
+  bottom: "Convolution2"
+  top: "Dropout1"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Dropout1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 2
+layer {
+  name: "DenseBlock2"
+  type: "DenseBlock"
+  bottom: "Pooling1"
+  top: "DenseBlock2"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm2"
+  type: "BatchNorm"
+  bottom: "DenseBlock2"
+  top: "BatchNorm2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+}
+
+
+
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "BatchNorm2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout2"
+  type: "Dropout"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution3"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 3
+layer {
+  name: "DenseBlock3"
+  type: "DenseBlock"
+  bottom: "Pooling2"
+  top: "DenseBlock3"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm3"
+  type: "BatchNorm"
+  bottom: "DenseBlock3"
+  top: "BatchNorm3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+}
+
+
+layer {
+  name: "pool5_ave"
+  type: "Pooling"
+  bottom: "BatchNorm3"
+  top: "pool5_ave"
+  pooling_param {
+    pool: AVE
+    kernel_w: 1
+	kernel_h: 4
+    stride_w: 1
+	stride_h: 1
+  }
+}
+
+layer {
+  name: "pool5_ave_transpose"
+  top: "pool5_ave_transpose"
+  bottom: "pool5_ave"
+  type: "Transpose"
+  transpose_param {
+    dim: 3
+    dim: 2
+    dim: 0
+    dim: 1
+  }
+}
+
+layer {
+  name: "blstm_input"
+  type: "Reshape"
+  bottom: "pool5_ave_transpose"
+  top: "blstm_input"
+  reshape_param {
+    shape { dim: -1 }
+    axis: 1
+    num_axes: 2
+  }
+}
+
+#===================blstm layer 1============================
+#======lstm1===================
+layer {
+  name: "lstm1"
+  type: "Lstm"
+  bottom: "blstm_input"
+  top: "lstm1"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm1_reverse===================
+layer {
+  name: "lstm1-reverse1"
+  type: "Reverse"
+  bottom: "blstm_input"
+  top: "rlstm1_input"
+  reverse_param {
+    axis: 0
+  }
+}
+layer {
+  name: "rlstm1"
+  type: "Lstm"
+  bottom: "rlstm1_input"
+  top: "rlstm1-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm1-reverse2"
+  type: "Reverse"
+  bottom: "rlstm1-output"
+  top: "rlstm1"
+  reverse_param {
+    axis: 0
+  }
+}
+
+
+# merge lstm1 and rlstm1
+layer {
+  name: "blstm1"
+  type: "Eltwise"
+  bottom: "lstm1"
+  bottom: "rlstm1"
+  bottom: "blstm_input"
+  top: "blstm1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+
+ 
+
+#===================blstm layer 2============================
+#======lstm2===================
+layer {
+  name: "lstm2"
+  type: "Lstm"
+  bottom: "blstm1"
+  top: "lstm2"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm2_reverse===================
+layer {
+  name: "lstm2-reverse1"
+  type: "Reverse"
+  bottom: "blstm1"
+  top: "rlstm2_input"
+  reverse_param {
+    axis: 0
+  }
+}
+
+layer {
+  name: "rlstm2"
+  type: "Lstm"
+  bottom: "rlstm2_input"
+  top: "rlstm2-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm2-reverse2"
+  type: "Reverse"
+  bottom: "rlstm2-output"
+  top: "rlstm2"
+  reverse_param {
+    axis: 0
+  }
+}
+
+# merge lstm2 and rlstm2
+layer {
+  name: "blstm2"
+  type: "Eltwise"
+  bottom: "lstm2"
+  bottom: "rlstm2"
+  bottom: "blstm1"
+  bottom: "blstm_input"
+  top: "blstm2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+layer {
+  name: "fc1x"
+  type: "InnerProduct"
+  bottom: "blstm2"
+  top: "fc1x"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    axis: 2
+    num_output: 21
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+  name: "ctcloss"
+  type: "WarpCTCLoss"
+  bottom: "fc1x"
+  bottom: "label"
+  top: "ctcloss"
+  loss_weight:1
+}
+
+layer {
+	name: "acc"
+	type: "CTCGreedyDecoder"
+	bottom: "fc1x"
+	bottom: "label"
+	top: "acc"
+	include {
+    phase: TEST
+  }
+}
\ No newline at end of file
diff --git a/ocr_test/wm_test/deploy.prototxt b/ocr_test/wm_test/deploy.prototxt
new file mode 100644
index 0000000000000000000000000000000000000000..64ebb627666059701ccd93945c2e1e9c4bcc8198
--- /dev/null
+++ b/ocr_test/wm_test/deploy.prototxt
@@ -0,0 +1,514 @@
+name: "densenet"
+
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 32
+input_dim: 280
+
+
+layer {
+  bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+	param {
+		lr_mult: 1
+		decay_mult: 1
+    }
+    convolution_param {
+    num_output: 64
+      kernel_size: 5
+      pad: 2
+      stride: 2
+	  weight_filler { type: "msra"}
+	  bias_filler { type: "constant" value: 0 }
+      }
+}
+
+
+# DenseBlock 1
+layer {
+  name: "DenseBlock1"
+  type: "DenseBlock"
+  bottom: "conv1"
+  top: "DenseBlock1"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm1"
+  type: "BatchNorm"
+  bottom: "DenseBlock1"
+  top: "BatchNorm1"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "BatchNorm1"
+  top: "BatchNorm1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "BatchNorm1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout1"
+  type: "Dropout"
+  bottom: "Convolution2"
+  top: "Dropout1"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Dropout1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 2
+layer {
+  name: "DenseBlock2"
+  type: "DenseBlock"
+  bottom: "Pooling1"
+  top: "DenseBlock2"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm2"
+  type: "BatchNorm"
+  bottom: "DenseBlock2"
+  top: "BatchNorm2"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "BatchNorm2"
+  top: "BatchNorm2"
+}
+
+
+
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "BatchNorm2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "Dropout2"
+  type: "Dropout"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  dropout_param {
+    dropout_ratio: 0.2
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution3"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+
+# DenseBlock 3
+layer {
+  name: "DenseBlock3"
+  type: "DenseBlock"
+  bottom: "Pooling2"
+  top: "DenseBlock3"
+  denseblock_param {
+    numTransition: 8
+    initChannel: 64
+    growthRate: 8
+    Filter_Filler {
+      type: "msra"
+    }
+    BN_Scaler_Filler {
+      type: "constant"
+      value: 1
+    }
+    BN_Bias_Filler {
+      type: "constant"
+      value: 0
+    }
+    use_dropout: false
+    dropout_amount: 0.2
+  }
+}
+layer {
+  name: "BatchNorm3"
+  type: "BatchNorm"
+  bottom: "DenseBlock3"
+  top: "BatchNorm3"
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 0
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+  scale_param {
+    filler {
+      value: 1
+    }
+    bias_term: true
+    bias_filler {
+      value: 0
+    }
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "BatchNorm3"
+  top: "BatchNorm3"
+}
+
+
+layer {
+  name: "pool5_ave"
+  type: "Pooling"
+  bottom: "BatchNorm3"
+  top: "pool5_ave"
+  pooling_param {
+    pool: AVE
+    kernel_w: 1
+	kernel_h: 4
+    stride_w: 1
+	stride_h: 1
+  }
+}
+
+layer {
+  name: "pool5_ave_transpose"
+  top: "pool5_ave_transpose"
+  bottom: "pool5_ave"
+  type: "Transpose"
+  transpose_param {
+    dim: 3
+    dim: 2
+    dim: 0
+    dim: 1
+  }
+}
+
+layer {
+  name: "blstm_input"
+  type: "Reshape"
+  bottom: "pool5_ave_transpose"
+  top: "blstm_input"
+  reshape_param {
+    shape { dim: -1 }
+    axis: 1
+    num_axes: 2
+  }
+}
+
+#===================blstm layer 1============================
+#======lstm1===================
+layer {
+  name: "lstm1"
+  type: "Lstm"
+  bottom: "blstm_input"
+  top: "lstm1"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm1_reverse===================
+layer {
+  name: "lstm1-reverse1"
+  type: "Reverse"
+  bottom: "blstm_input"
+  top: "rlstm1_input"
+  reverse_param {
+    axis: 0
+  }
+}
+layer {
+  name: "rlstm1"
+  type: "Lstm"
+  bottom: "rlstm1_input"
+  top: "rlstm1-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm1-reverse2"
+  type: "Reverse"
+  bottom: "rlstm1-output"
+  top: "rlstm1"
+  reverse_param {
+    axis: 0
+  }
+}
+
+
+# merge lstm1 and rlstm1
+layer {
+  name: "blstm1"
+  type: "Eltwise"
+  bottom: "lstm1"
+  bottom: "rlstm1"
+  bottom: "blstm_input"
+  top: "blstm1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+
+ 
+
+#===================blstm layer 2============================
+#======lstm2===================
+layer {
+  name: "lstm2"
+  type: "Lstm"
+  bottom: "blstm1"
+  top: "lstm2"
+  lstm_param {
+    num_output: 256
+    weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+# =====lstm2_reverse===================
+layer {
+  name: "lstm2-reverse1"
+  type: "Reverse"
+  bottom: "blstm1"
+  top: "rlstm2_input"
+  reverse_param {
+    axis: 0
+  }
+}
+
+layer {
+  name: "rlstm2"
+  type: "Lstm"
+  bottom: "rlstm2_input"
+  top: "rlstm2-output"
+  lstm_param {
+    num_output: 256
+	weight_filler {
+      type: "gaussian"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+    }
+   }
+}
+layer {
+  name: "lstm2-reverse2"
+  type: "Reverse"
+  bottom: "rlstm2-output"
+  top: "rlstm2"
+  reverse_param {
+    axis: 0
+  }
+}
+
+# merge lstm2 and rlstm2
+layer {
+  name: "blstm2"
+  type: "Eltwise"
+  bottom: "lstm2"
+  bottom: "rlstm2"
+  bottom: "blstm1"
+  bottom: "blstm_input"
+  top: "blstm2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+
+layer {
+  name: "fc1x"
+  type: "InnerProduct"
+  bottom: "blstm2"
+  top: "fc1x"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    axis: 2
+    num_output: 21
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+	name: "result"
+	type: "CTCGreedyDecoder"
+	bottom: "fc1x"
+	top: "result"
+}
\ No newline at end of file
diff --git a/ocr_test/wm_test/label.txt b/ocr_test/wm_test/label.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1e9cc8d98a175858fa8e69bcf5019e90eb4e34ea
--- /dev/null
+++ b/ocr_test/wm_test/label.txt
@@ -0,0 +1,21 @@
+blank
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
\ No newline at end of file
diff --git a/ocr_test/wm_test/mean_values.txt b/ocr_test/wm_test/mean_values.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f2c94c6579fc063bea87dec00bad89680fcf6473
--- /dev/null
+++ b/ocr_test/wm_test/mean_values.txt
@@ -0,0 +1,3 @@
+152
+152
+152
\ No newline at end of file
diff --git a/ocr_test/wm_test/model.caffemodel b/ocr_test/wm_test/model.caffemodel
new file mode 100644
index 0000000000000000000000000000000000000000..aa4704627999324b1b9d69d0a4c7cef94af4d049
Binary files /dev/null and b/ocr_test/wm_test/model.caffemodel differ
diff --git a/ocr_test/x64/Release/bktree.obj b/ocr_test/x64/Release/bktree.obj
new file mode 100644
index 0000000000000000000000000000000000000000..2547ad03e825db81427c388c153bba98d503dea1
Binary files /dev/null and b/ocr_test/x64/Release/bktree.obj differ
diff --git a/ocr_test/x64/Release/ctcpp_entrypoint.obj b/ocr_test/x64/Release/ctcpp_entrypoint.obj
new file mode 100644
index 0000000000000000000000000000000000000000..83f6c0427d280fd1a9644ea449cd3419cf78512e
Binary files /dev/null and b/ocr_test/x64/Release/ctcpp_entrypoint.obj differ
diff --git a/ocr_test/x64/Release/levenshtein.obj b/ocr_test/x64/Release/levenshtein.obj
new file mode 100644
index 0000000000000000000000000000000000000000..ca17392b8769b1b5777dbaff4943904fa083dbde
Binary files /dev/null and b/ocr_test/x64/Release/levenshtein.obj differ
diff --git a/ocr_test/x64/Release/ocr_test.Build.CppClean.log b/ocr_test/x64/Release/ocr_test.Build.CppClean.log
new file mode 100644
index 0000000000000000000000000000000000000000..afcb0c1848fd56b858aa609803f2ff8ce456f058
--- /dev/null
+++ b/ocr_test/x64/Release/ocr_test.Build.CppClean.log
@@ -0,0 +1,21 @@
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.pch
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\vc140.pdb
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\stdafx.obj
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\levenshtein.obj
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\bktree.obj
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ctcpp_entrypoint.obj
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.obj
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\tools_bin\ocr_test.exe
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\tools_bin\ocr_test.ipdb
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\tools_bin\ocr_test.iobj
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\tools_bin\ocr_test.pdb
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\tools_bin\ocr_test.lib
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\tools_bin\ocr_test.exp
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\..\..\tools_bin\ocr_test.exe
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\cl.command.1.tlog
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\cl.read.1.tlog
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\cl.write.1.tlog
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\link.command.1.tlog
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\link.read.1.tlog
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\link.write.1.tlog
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\caffe-vsproj\ocr_test\x64\release\ocr_test.tlog\ocr_test.write.1u.tlog
diff --git a/ocr_test/x64/Release/ocr_test.log b/ocr_test/x64/Release/ocr_test.log
new file mode 100644
index 0000000000000000000000000000000000000000..0509119567ebe2ece2e5630bba79a1292d5a7bec
--- /dev/null
+++ b/ocr_test/x64/Release/ocr_test.log
@@ -0,0 +1,275 @@
+  ocr_test.cpp
+ocr_test.cpp(207): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+ocr_test.cpp(208): warning C4244: “初始化”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(229): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+ocr_test.cpp(310): warning C4267: “参数”: 从“size_t”转换到“unsigned char”，可能丢失数据
+ocr_test.cpp(315): warning C4267: “参数”: 从“size_t”转换到“unsigned char”，可能丢失数据
+ocr_test.cpp(324): warning C4267: “参数”: 从“size_t”转换到“unsigned char”，可能丢失数据
+ocr_test.cpp(376): warning C4267: “参数”: 从“size_t”转换到“unsigned char”，可能丢失数据
+ocr_test.cpp(385): warning C4267: “参数”: 从“size_t”转换到“int”，可能丢失数据
+ocr_test.cpp(370): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 1 拥有了类型“std::size_t”
+  ocr_test.cpp(370): note: 请考虑在格式字符串中使用“%zd”
+ocr_test.cpp(370): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 2 拥有了类型“unsigned __int64”
+  ocr_test.cpp(370): note: 请考虑在格式字符串中使用“%zd”
+ocr_test.cpp(477): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 1 拥有了类型“std::size_t”
+  ocr_test.cpp(477): note: 请考虑在格式字符串中使用“%zd”
+ocr_test.cpp(477): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 2 拥有了类型“unsigned __int64”
+  ocr_test.cpp(477): note: 请考虑在格式字符串中使用“%zd”
+  Unknown compiler version - please run the configure tests and report the results
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\include\caffe/util/io.hpp(49): warning C4244: “参数”: 从“uint64_t”转换到“int”，可能丢失数据
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(68): warning C4251: “MTCNN::PNet_”: class“boost::shared_ptr<caffe::Net<float>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(58): note: 参见“boost::shared_ptr<caffe::Net<float>>”的声明
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(69): warning C4251: “MTCNN::RNet_”: class“boost::shared_ptr<caffe::Net<float>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(58): note: 参见“boost::shared_ptr<caffe::Net<float>>”的声明
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(70): warning C4251: “MTCNN::ONet_”: class“boost::shared_ptr<caffe::Net<float>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(58): note: 参见“boost::shared_ptr<caffe::Net<float>>”的声明
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(73): warning C4251: “MTCNN::condidate_rects_”: class“std::vector<FaceInfo,std::allocator<_Ty>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+          with
+          [
+              _Ty=FaceInfo
+          ]
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(46): note: 参见“std::vector<FaceInfo,std::allocator<_Ty>>”的声明
+          with
+          [
+              _Ty=FaceInfo
+          ]
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(74): warning C4251: “MTCNN::total_boxes_”: class“std::vector<FaceInfo,std::allocator<_Ty>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+          with
+          [
+              _Ty=FaceInfo
+          ]
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(46): note: 参见“std::vector<FaceInfo,std::allocator<_Ty>>”的声明
+          with
+          [
+              _Ty=FaceInfo
+          ]
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(75): warning C4251: “MTCNN::regressed_rects_”: class“std::vector<FaceInfo,std::allocator<_Ty>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+          with
+          [
+              _Ty=FaceInfo
+          ]
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(46): note: 参见“std::vector<FaceInfo,std::allocator<_Ty>>”的声明
+          with
+          [
+              _Ty=FaceInfo
+          ]
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(76): warning C4251: “MTCNN::regressed_pading_”: class“std::vector<FaceInfo,std::allocator<_Ty>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+          with
+          [
+              _Ty=FaceInfo
+          ]
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(46): note: 参见“std::vector<FaceInfo,std::allocator<_Ty>>”的声明
+          with
+          [
+              _Ty=FaceInfo
+          ]
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\caffe-gpu.h(78): warning C4251: “MTCNN::crop_img_”: class“std::vector<cv::Mat,std::allocator<_Ty>>”需要有 dll 接口由 class“MTCNN”的客户端使用
+          with
+          [
+              _Ty=cv::Mat
+          ]
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\opensource\opencv\include\opencv2/core/mat.hpp(183): note: 参见“std::vector<cv::Mat,std::allocator<_Ty>>”的声明
+          with
+          [
+              _Ty=cv::Mat
+          ]
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(3): warning C4005: “_CRT_SECURE_NO_WARNINGS”: 宏重定义
+  d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(3): note: 命令行参数: 参见前面的“_CRT_SECURE_NO_WARNINGS”定义
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(228): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(232): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(261): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(272): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(273): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(293): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(303): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(304): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(367): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(385): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(387): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(405): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(406): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(424): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(440): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(441): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(461): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(481): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(482): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(500): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(512): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(513): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(561): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(595): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(596): warning C4267: “=”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(604): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(607): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(680): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(714): warning C4244: “初始化”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(746): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(752): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(772): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(835): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(841): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(854): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(869): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(882): warning C4244: “=”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(1106): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(1748): warning C4244: “参数”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(1749): warning C4244: “参数”: 从“double”转换到“int”，可能丢失数据
+d:\ocr\plate_card_blstm\caffe_ocr-master_cudn7_cuda9.2_vs2014_wm_platecard\caffe-vsproj\ocr_test\common.h(1769): warning C4244: “return”: 从“__int64”转换到“double”，可能丢失数据
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\classifierCaffe.h(41): warning C4251: “Classifier::input_geometry_”: class“cv::Size_<int>”需要有 dll 接口由 class“Classifier”的客户端使用
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\opensource\opencv\include\opencv2/core/types.hpp(337): note: 参见“cv::Size_<int>”的声明
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\classifierCaffe.h(53): warning C4251: “Classifier::net_”: class“std::shared_ptr<caffe::Net<float>>”需要有 dll 接口由 class“Classifier”的客户端使用
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\classifierCaffe.h(53): note: 参见“std::shared_ptr<caffe::Net<float>>”的声明
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\classifierCaffe.h(56): warning C4251: “Classifier::mean_”: class“cv::Mat”需要有 dll 接口由 class“Classifier”的客户端使用
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\include\caffe/common.hpp(73): note: 参见“cv::Mat”的声明
+D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\classifierCaffe.h(57): warning C4251: “Classifier::labels_”: class“std::vector<std::vector<std::string,std::allocator<_Ty>>,std::allocator<std::vector<_Ty,std::allocator<_Ty>>>>”需要有 dll 接口由 class“Classifier”的客户端使用
+          with
+          [
+              _Ty=std::string
+          ]
+  D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\libClassification\classifierCaffe.h(57): note: 参见“std::vector<std::vector<std::string,std::allocator<_Ty>>,std::allocator<std::vector<_Ty,std::allocator<_Ty>>>>”的声明
+          with
+          [
+              _Ty=std::string
+          ]
+ocr_test.cpp(704): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+ocr_test.cpp(814): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(843): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(844): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(845): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(846): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(910): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(913): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(995): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+ocr_test.cpp(1126): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(1155): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1156): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1157): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1158): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1251): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(1254): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(1395): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+ocr_test.cpp(1528): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(1557): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1558): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1559): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1560): warning C4244: “参数”: 从“int”转换到“float”，可能丢失数据
+ocr_test.cpp(1620): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(1623): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(1636): warning C4244: “参数”: 从“float”转换到“int”，可能丢失数据
+ocr_test.cpp(2093): warning C4244: “参数”: 从“double”转换到“int”，可能丢失数据
+ocr_test.cpp(1659): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 1 拥有了类型“unsigned __int64”
+  ocr_test.cpp(1659): note: 请考虑在格式字符串中使用“%zd”
+ocr_test.cpp(1699): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 1 拥有了类型“unsigned __int64”
+  ocr_test.cpp(1699): note: 请考虑在格式字符串中使用“%zd”
+ocr_test.cpp(1702): warning C4477: “printf”: 格式字符串“%d”需要类型“int”的参数，但可变参数 1 拥有了类型“unsigned __int64”
+  ocr_test.cpp(1702): note: 请考虑在格式字符串中使用“%zd”
+ocr_test.cpp(1750): warning C4101: “predict6”: 未引用的局部变量
+ocr_test.cpp(1754): warning C4101: “loss6”: 未引用的局部变量
+C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\utility(200): warning C4267: “初始化”: 从“size_t”转换到“int”，可能丢失数据
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xmemory0(737): note: 参见对正在编译的函数 模板 实例化“std::pair<const _Kty,_Ty>::pair<wchar_t,unsigned __int64,void,0>(std::pair<wchar_t,unsigned __int64> &&) noexcept”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xmemory0(737): note: 参见对正在编译的函数 模板 实例化“std::pair<const _Kty,_Ty>::pair<wchar_t,unsigned __int64,void,0>(std::pair<wchar_t,unsigned __int64> &&) noexcept”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xmemory0(857): note: 参见对正在编译的函数 模板 实例化“void std::allocator<_Other>::construct<_Objty,_Ty>(_Objty *,_Ty &&)”的引用
+          with
+          [
+              _Other=std::_Tree_node<std::pair<const wchar_t,int>,void *>,
+              _Objty=std::pair<const wchar_t,int>,
+              _Ty=std::pair<wchar_t,unsigned __int64>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xmemory0(857): note: 参见对正在编译的函数 模板 实例化“void std::allocator<_Other>::construct<_Objty,_Ty>(_Objty *,_Ty &&)”的引用
+          with
+          [
+              _Other=std::_Tree_node<std::pair<const wchar_t,int>,void *>,
+              _Objty=std::pair<const wchar_t,int>,
+              _Ty=std::pair<wchar_t,unsigned __int64>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xmemory0(996): note: 参见对正在编译的函数 模板 实例化“void std::allocator_traits<_Alloc>::construct<_Ty,std::pair<wchar_t,unsigned __int64>>(std::allocator<_Other> &,_Objty *,std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Alloc=std::allocator<std::_Tree_node<std::pair<const wchar_t,int>,void *>>,
+              _Ty=std::pair<const wchar_t,int>,
+              _Other=std::_Tree_node<std::pair<const wchar_t,int>,void *>,
+              _Objty=std::pair<const wchar_t,int>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xmemory0(995): note: 参见对正在编译的函数 模板 实例化“void std::allocator_traits<_Alloc>::construct<_Ty,std::pair<wchar_t,unsigned __int64>>(std::allocator<_Other> &,_Objty *,std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Alloc=std::allocator<std::_Tree_node<std::pair<const wchar_t,int>,void *>>,
+              _Ty=std::pair<const wchar_t,int>,
+              _Other=std::_Tree_node<std::pair<const wchar_t,int>,void *>,
+              _Objty=std::pair<const wchar_t,int>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xtree(889): note: 参见对正在编译的函数 模板 实例化“void std::_Wrap_alloc<std::allocator<_Other>>::construct<_Ty,std::pair<wchar_t,unsigned __int64>>(_Ty *,std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Other=std::_Tree_node<std::pair<const wchar_t,int>,void *>,
+              _Ty=std::pair<const wchar_t,int>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xtree(887): note: 参见对正在编译的函数 模板 实例化“void std::_Wrap_alloc<std::allocator<_Other>>::construct<_Ty,std::pair<wchar_t,unsigned __int64>>(_Ty *,std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Other=std::_Tree_node<std::pair<const wchar_t,int>,void *>,
+              _Ty=std::pair<const wchar_t,int>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xtree(1076): note: 参见对正在编译的函数 模板 实例化“std::_Tree_node<std::pair<const _Kty,_Ty>,void *> *std::_Tree_comp_alloc<_Traits>::_Buynode<std::pair<wchar_t,unsigned __int64>>(std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int,
+              _Traits=std::_Tmap_traits<wchar_t,int,std::less<wchar_t>,std::allocator<std::pair<const wchar_t,int>>,false>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xtree(1076): note: 参见对正在编译的函数 模板 实例化“std::_Tree_node<std::pair<const _Kty,_Ty>,void *> *std::_Tree_comp_alloc<_Traits>::_Buynode<std::pair<wchar_t,unsigned __int64>>(std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int,
+              _Traits=std::_Tmap_traits<wchar_t,int,std::less<wchar_t>,std::allocator<std::pair<const wchar_t,int>>,false>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\map(194): note: 参见对正在编译的函数 模板 实例化“std::pair<std::_Tree_iterator<std::_Tree_val<std::_Tree_simple_types<std::pair<const _Kty,_Ty>>>>,bool> std::_Tree<std::_Tmap_traits<_Kty,_Ty,_Pr,_Alloc,false>>::emplace<std::pair<wchar_t,unsigned __int64>>(std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int,
+              _Pr=std::less<wchar_t>,
+              _Alloc=std::allocator<std::pair<const wchar_t,int>>
+          ]
+  C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\map(194): note: 参见对正在编译的函数 模板 实例化“std::pair<std::_Tree_iterator<std::_Tree_val<std::_Tree_simple_types<std::pair<const _Kty,_Ty>>>>,bool> std::_Tree<std::_Tmap_traits<_Kty,_Ty,_Pr,_Alloc,false>>::emplace<std::pair<wchar_t,unsigned __int64>>(std::pair<wchar_t,unsigned __int64> &&)”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int,
+              _Pr=std::less<wchar_t>,
+              _Alloc=std::allocator<std::pair<const wchar_t,int>>
+          ]
+  ocr_test.cpp(292): note: 参见对正在编译的函数 模板 实例化“std::pair<std::_Tree_iterator<std::_Tree_val<std::_Tree_simple_types<std::pair<const _Kty,_Ty>>>>,bool> std::map<_Kty,_Ty,std::less<_Kty>,std::allocator<std::pair<const _Kty,_Ty>>>::insert<std::pair<wchar_t,unsigned __int64>,void>(_Valty &&)”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int,
+              _Valty=std::pair<wchar_t,unsigned __int64>
+          ]
+  ocr_test.cpp(292): note: 参见对正在编译的函数 模板 实例化“std::pair<std::_Tree_iterator<std::_Tree_val<std::_Tree_simple_types<std::pair<const _Kty,_Ty>>>>,bool> std::map<_Kty,_Ty,std::less<_Kty>,std::allocator<std::pair<const _Kty,_Ty>>>::insert<std::pair<wchar_t,unsigned __int64>,void>(_Valty &&)”的引用
+          with
+          [
+              _Kty=wchar_t,
+              _Ty=int,
+              _Valty=std::pair<wchar_t,unsigned __int64>
+          ]
+    正在创建库 ..\..\tools_bin\ocr_test.lib 和对象 ..\..\tools_bin\ocr_test.exp
+  正在生成代码
+  8 of 1358 functions ( 0.6%) were compiled, the rest were copied from previous compilation.
+    0 functions were new in current compilation
+    74 functions had inline decision re-evaluated but remain unchanged
+  已完成代码的生成
+  ocr_test.vcxproj -> D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\ocr_test\..\..\tools_bin\ocr_test.exe
+  ocr_test.vcxproj -> ..\..\tools_bin\ocr_test.pdb (Full PDB)
diff --git a/ocr_test/x64/Release/ocr_test.obj b/ocr_test/x64/Release/ocr_test.obj
new file mode 100644
index 0000000000000000000000000000000000000000..7477247228d6dab540cbb2db70148dcda5886e70
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diff --git a/ocr_test/x64/Release/ocr_test.pch b/ocr_test/x64/Release/ocr_test.pch
new file mode 100644
index 0000000000000000000000000000000000000000..fdffaa251b05f70a2417bc643af080619aba48fb
Binary files /dev/null and b/ocr_test/x64/Release/ocr_test.pch differ
diff --git a/ocr_test/x64/Release/ocr_test.tlog/CL.command.1.tlog b/ocr_test/x64/Release/ocr_test.tlog/CL.command.1.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..8eab66b6c430ab85bc684d083872bb704baefbbe
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diff --git a/ocr_test/x64/Release/ocr_test.tlog/CL.read.1.tlog b/ocr_test/x64/Release/ocr_test.tlog/CL.read.1.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..f2647023f0e1f5f7212b508f4bb9d3efef46827e
Binary files /dev/null and b/ocr_test/x64/Release/ocr_test.tlog/CL.read.1.tlog differ
diff --git a/ocr_test/x64/Release/ocr_test.tlog/CL.write.1.tlog b/ocr_test/x64/Release/ocr_test.tlog/CL.write.1.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..222fbb12f519a6a7939412f1f77e08b486334e03
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diff --git a/ocr_test/x64/Release/ocr_test.tlog/link.command.1.tlog b/ocr_test/x64/Release/ocr_test.tlog/link.command.1.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..a8f7259f94b4474a9f949647727b2bde09a6c99c
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diff --git a/ocr_test/x64/Release/ocr_test.tlog/link.read.1.tlog b/ocr_test/x64/Release/ocr_test.tlog/link.read.1.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..be9e5165baa29b60439da68369aa614fde32a051
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diff --git a/ocr_test/x64/Release/ocr_test.tlog/link.write.1.tlog b/ocr_test/x64/Release/ocr_test.tlog/link.write.1.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..d21529c5f2a10b5d98012faf95e9ba09f60f31ca
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diff --git a/ocr_test/x64/Release/ocr_test.tlog/ocr_test.lastbuildstate b/ocr_test/x64/Release/ocr_test.tlog/ocr_test.lastbuildstate
new file mode 100644
index 0000000000000000000000000000000000000000..bc88a62314826d45ac78c7c5482fe97297237b8f
--- /dev/null
+++ b/ocr_test/x64/Release/ocr_test.tlog/ocr_test.lastbuildstate
@@ -0,0 +1,2 @@
+#TargetFrameworkVersion=v4.0:PlatformToolSet=v140:EnableManagedIncrementalBuild=false:VCToolArchitecture=Native32Bit:WindowsTargetPlatformVersion=8.1
+Release|x64|D:\ocr\plate_card_BLSTM\caffe_ocr-master_CUDN7_CUDA9.2_vs2014_WM_plateCard\caffe-vsproj\|
diff --git a/ocr_test/x64/Release/ocr_test.tlog/ocr_test.write.1u.tlog b/ocr_test/x64/Release/ocr_test.tlog/ocr_test.write.1u.tlog
new file mode 100644
index 0000000000000000000000000000000000000000..4e1a8aebf1f8f9388525186ff7c11a379f91e046
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diff --git a/ocr_test/x64/Release/stdafx.obj b/ocr_test/x64/Release/stdafx.obj
new file mode 100644
index 0000000000000000000000000000000000000000..4b3bb314269640474738bddbd2474a2c472eb46d
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diff --git a/ocr_test/x64/Release/vc140.pdb b/ocr_test/x64/Release/vc140.pdb
new file mode 100644
index 0000000000000000000000000000000000000000..b3ddefefbf3ff04accd81f0a106cb4f33f325aa4
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diff --git "a/result_plateCard/QQ\345\233\276\347\211\20720180529195834.png" "b/result_plateCard/QQ\345\233\276\347\211\20720180529195834.png"
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