【opencv】神经网络识别字母+数字

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 
#include 
#include 
#include 

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"<((int)k/8,(int)k%8);//(float)train_image.data[k];
                //cout<(k/8,k%8)<(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...."<sampleMat(1,image_rows*image_cols); 
	for(int i = 0; i(0,i) = (float)test_temp.at(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："<