继承自本人博客：
http://blog.csdn.net/qq_15947787/article/details/51385861
原文只是识别数字0-9，简单修改后可以识别24个字母（除了I，O）与数字。
把0与O看成一类，1与I看成一类
附件从原文下载即可。
//opencv2.4.9 + vs2012 + 64位
#include <windows.h>
#include <iostream>
#include <fstream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
char* WcharToChar(const wchar_t* wp)
{
char *m_char;
int len= WideCharToMultiByte(CP_ACP,0,wp,wcslen(wp),NULL,0,NULL,NULL);
m_char=new char[len+1];
WideCharToMultiByte(CP_ACP,0,wp,wcslen(wp),m_char,len,NULL,NULL);
m_char[len]='\0';
return m_char;
}
wchar_t* CharToWchar(const char* c)
{
wchar_t *m_wchar;
int len = MultiByteToWideChar(CP_ACP,0,c,strlen(c),NULL,0);
m_wchar=new wchar_t[len+1];
MultiByteToWideChar(CP_ACP,0,c,strlen(c),m_wchar,len);
m_wchar[len]='\0';
return m_wchar;
}
wchar_t* StringToWchar(const string& s)
{
const char* p=s.c_str();
return CharToWchar(p);
}
int main()
{
const string fileform = "*.png";
const string perfileReadPath = "charSamples";
const int sample_mun_perclass = 20;//训练字符每类数量
const int class_mun = 10+26;//训练字符类数 0-9 A-Z 除了I、O
const int image_cols = 8;
const int image_rows = 16;
string fileReadName,
fileReadPath;
char temp[256];
float trainingData[class_mun*sample_mun_perclass][image_rows*image_cols] = {{0}};//每一行一个训练样本
float labels[class_mun*sample_mun_perclass][class_mun]={{0}};//训练样本标签
for(int i = 0; i <= class_mun - 1; i++)//不同类
{
//读取每个类文件夹下所有图像
int j = 0;//每一类读取图像个数计数
if (i <= 9)//0-9
{
sprintf(temp, "%d", i);
//printf("%d\n", i);
}
else//A-Z
{
sprintf(temp, "%c", i + 55);
//printf("%c\n", i+55);
}
fileReadPath = perfileReadPath + "/" + temp + "/" + fileform;
cout<<"文件夹"<<temp<<endl;
HANDLE hFile;
LPCTSTR lpFileName = StringToWchar(fileReadPath);//指定搜索目录和文件类型，如搜索d盘的音频文件可以是"D:\\*.mp3"
WIN32_FIND_DATA pNextInfo; //搜索得到的文件信息将储存在pNextInfo中;
hFile = FindFirstFile(lpFileName,&pNextInfo);//请注意是 &pNextInfo , 不是 pNextInfo;
if(hFile == INVALID_HANDLE_VALUE)
{
continue;//搜索失败
}
//do-while循环读取
do
{
if(pNextInfo.cFileName[0] == '.')//过滤.和..
continue;
j++;//读取一张图
//wcout<<pNextInfo.cFileName<<endl;
//printf("%s\n",WcharToChar(pNextInfo.cFileName));
//对读入的图片进行处理
Mat srcImage = imread( perfileReadPath + "/" + temp + "/" + WcharToChar(pNextInfo.cFileName),CV_LOAD_IMAGE_GRAYSCALE);
Mat resizeImage;
Mat trainImage;
Mat result;
resize(srcImage,resizeImage,Size(image_cols,image_rows),(0,0),(0,0),CV_INTER_AREA);//使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
threshold(resizeImage,trainImage,0,255,CV_THRESH_BINARY|CV_THRESH_OTSU);
for(int k = 0; k<image_rows*image_cols; ++k)
{
trainingData[i*sample_mun_perclass+(j-1)][k] = (float)trainImage.data[k];
//trainingData[i*sample_mun_perclass+(j-1)][k] = (float)trainImage.at<unsigned char>((int)k/8,(int)k%8);//(float)train_image.data[k];
//cout<<trainingData[i*sample_mun_perclass+(j-1)][k] <<" "<< (float)trainImage.at<unsigned char>(k/8,k%8)<<endl;
}
}while (FindNextFile(hFile,&pNextInfo) && j<sample_mun_perclass);//如果设置读入的图片数量，则以设置的为准，如果图片不够，则读取文件夹下所有图片
}
// Set up training data Mat
Mat trainingDataMat(class_mun*sample_mun_perclass, image_rows*image_cols, CV_32FC1, trainingData);
cout<<"trainingDataMat——OK！"<<endl;
// Set up label data
for(int i = 0;i <= class_mun-1; ++i)
{
for(int j = 0;j <= sample_mun_perclass - 1; ++j)
{
for(int k = 0;k < class_mun; ++k)
{
if(k == i)
if (k == 18)
{
labels[i*sample_mun_perclass + j][1] = 1;
}
else if(k == 24)
{
labels[i*sample_mun_perclass + j][0] = 1;
}
else
{
labels[i*sample_mun_perclass + j][k] = 1;
}
else
labels[i*sample_mun_perclass + j][k] = 0;
}
}
}
Mat labelsMat(class_mun*sample_mun_perclass, class_mun, CV_32FC1,labels);
cout<<"labelsMat:"<<endl;
ofstream outfile("out.txt");
outfile<<labelsMat;
//cout<<labelsMat<<endl;
cout<<"labelsMat——OK！"<<endl;
//训练代码
cout<<"training start...."<<endl;
CvANN_MLP bp;
// Set up BPNetwork's parameters
CvANN_MLP_TrainParams params;
params.train_method=CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale=0.001;
params.bp_moment_scale=0.1;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,10000,0.0001); //设置结束条件
//params.train_method=CvANN_MLP_TrainParams::RPROP;
//params.rp_dw0 = 0.1;
//params.rp_dw_plus = 1.2;
//params.rp_dw_minus = 0.5;
//params.rp_dw_min = FLT_EPSILON;
//params.rp_dw_max = 50.;
//Setup the BPNetwork
Mat layerSizes=(Mat_<int>(1,5) << image_rows*image_cols,128,128,128,class_mun);
bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM,1.0,1.0);//CvANN_MLP::SIGMOID_SYM
//CvANN_MLP::GAUSSIAN
//CvANN_MLP::IDENTITY
cout<<"training...."<<endl;
bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);
bp.save("../bpcharModel.xml"); //save classifier
cout<<"training finish...bpModel1.xml saved "<<endl;
//测试神经网络
cout<<"测试："<<endl;
Mat test_image = imread("test4.png",CV_LOAD_IMAGE_GRAYSCALE);
Mat test_temp;
resize(test_image,test_temp,Size(image_cols,image_rows),(0,0),(0,0),CV_INTER_AREA);//使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现
threshold(test_temp,test_temp,0,255,CV_THRESH_BINARY|CV_THRESH_OTSU);
Mat_<float>sampleMat(1,image_rows*image_cols);
for(int i = 0; i<image_rows*image_cols; ++i)
{
sampleMat.at<float>(0,i) = (float)test_temp.at<uchar>(i/8,i%8);
}
Mat responseMat;
bp.predict(sampleMat,responseMat);
Point maxLoc;
double maxVal = 0;
minMaxLoc(responseMat,NULL,&maxVal,NULL,&maxLoc);
if (maxLoc.x <= 9)//0-9
{
sprintf(temp, "%d", maxLoc.x);
//printf("%d\n", i);
}
else//A-Z
{
sprintf(temp, "%c", maxLoc.x + 55);
//printf("%c\n", i+55);
}
cout<<"识别结果："<<temp<<" 相似度:"<<maxVal*100<<"%"<<endl;
imshow("test_image",test_image);
waitKey(0);
return 0;
}