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      之前说到,把车牌区域提前出来后,就可以着手识别程序了。先使用SVM判断是不是车牌。这里为了提高运行速度,板子资源有限,程序里我把svm训练部分注释掉了,假设每次都能找到车牌,实际使用时,还是要加上svm的。
      然后对图像进行分割,我们的分类器只能对数字一个一个地识别,所以把每个数字分割出来,每个字符归一化为20*20的字符。
      基本思想是先用findContours()函数把基本轮廓找出来,然后通过简单验证以确认是否为数字的轮廓。对于那些通过验证的轮廓,接下去会用boundingRect()找出它们的包围盒。
      分割完后就可以进行识别了,字符识别使用ANN算法采用三层神经网络,识别需要用到一些xml文件,这些文件需要用分类器和大量样本做训练,提取他们的特征、,让机器去“学习”(利用训练好的XML文件去预测图像中车牌 ),我找的这三个xml数据集,说实话,不太好用,准确率一般般,有兴趣的可以自己训练。

      完整程序如下,里面有详细注释了:

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <string>
#include <cvaux.h> 
#include <stdio.h> 
#include <opencv2/gpu/gpu.hpp>
#include <opencv2/ml/ml.hpp>

using namespace cv; 
using namespace std;

//车牌宽高比为520/110=4.727272左右,误差不超过40%
//车牌高度范围在15~125之间,视摄像头距离而定(图像大小)
bool verifySizes_closeImg(const RotatedRect & candidate)
{
    float error = 0.4;//误差40%
    const float aspect = 4.7272;//44/14; //长宽比
    int min = 15*aspect*15;//20*aspect*20; //面积下限,最小区域
    int max = 125*aspect*125;//180*aspect*180;  //面积上限,最大区域
    float rmin = aspect - aspect*error; //考虑误差后的最小长宽比
    float rmax = aspect + aspect*error; //考虑误差后的最大长宽比

    int area = candidate.size.height * candidate.size.width;//计算面积
    float r = (float)candidate.size.width/(float)candidate.size.height;//计算宽高比
    if(r <1)
        r = 1/r;

    if( (area < min || area > max) || (r< rmin || r > rmax)  )//满足条件才认为是车牌候选区域
        return false;
    else
        return true;
}

void RgbConvToGray(const Mat& inputImage,Mat & outpuImage)  //g = 0.3R+0.59G+0.11B
{
    outpuImage = Mat(inputImage.rows ,inputImage.cols ,CV_8UC1);  

    for (int i = 0 ;i<inputImage.rows ;++ i)
    {
        uchar *ptrGray = outpuImage.ptr<uchar>(i); 
        const Vec3b * ptrRgb = inputImage.ptr<Vec3b>(i);
        for (int j = 0 ;j<inputImage.cols ;++ j)
        {
            ptrGray[j] = 0.3*ptrRgb[j][2]+0.59*ptrRgb[j][1]+0.11*ptrRgb[j][0];  
        }
    }
}

void normal_area(Mat &intputImg, vector<RotatedRect> &rects_optimal, vector <Mat>& output_area )
{
    float r,angle;
    for (int i = 0 ;i< rects_optimal.size() ; ++i)
    {
        //旋转区域
        angle = rects_optimal[i].angle;
        r = (float)rects_optimal[i].size.width / (float) (float)rects_optimal[i].size.height;
        if(r<1)
            angle = 90 + angle;//旋转图像使其得到长大于高度图像。
        Mat rotmat = getRotationMatrix2D(rects_optimal[i].center , angle,1);//获得变形矩阵对象
        Mat img_rotated;
        warpAffine(intputImg ,img_rotated,rotmat, intputImg.size(),CV_INTER_CUBIC);
        imwrite("car_rotated.jpg",img_rotated);//得到旋转图像

        //裁剪图像
        Size rect_size = rects_optimal[i].size;
        if(r<1)
            swap(rect_size.width, rect_size.height); //交换高和宽
        Mat  img_crop;
        getRectSubPix(img_rotated ,rect_size,rects_optimal[i].center , img_crop );//图像切割

        //用光照直方图调整所有裁剪得到的图像,使具有相同宽度和高度,适用于训练和分类
        Mat resultResized;
        //别人写的:
        /*resultResized.create(33,144,CV32FC1);
        resize(img_crop , resultResized,resultResized.size() , 0,0,INTER_CUBIC);
        resultResized.convertTo(resultResized, CV32FC1);
        resultResized = resultResized.reshape(1,1);*/

        resultResized.create(33,144,CV_8UC3);//CV32FC1????
        resize(img_crop , resultResized,resultResized.size() , 0,0,INTER_CUBIC);
        Mat grayResult;
        RgbConvToGray(resultResized ,grayResult);
        //blur(grayResult ,grayResult,Size(3,3));
        equalizeHist(grayResult,grayResult);

        output_area.push_back(grayResult);
    }
}

bool char_verifySizes(const RotatedRect & candidate)
{
	float aspect = 45.0f/77.0f;//45.0f/90.0f;
	float width,height;
	if (candidate.size.width >=candidate.size.height)
	{
		width = (float) candidate.size.height;
		height = (float) candidate.size.width;
	}

	else 
	{
		width = (float) candidate.size.width;
		height  = (float)candidate.size.height;
	}
	//这样确定是了高比宽要高

	float charAspect = (float) width/ (float)height;//宽高比

	float error = 0.35;//0.5;
	float minHeight = 15;  //最小高度11
	float maxHeight = 28;//33;  //最大高度33

	float minAspect = 0.15;//0.05;  //考虑到数字1,最小长宽比为0.15
	float maxAspect = 1.0;

	if( charAspect > minAspect && charAspect <= 1.0
		&&  height>= minHeight && height< maxHeight) //非0像素maxAspect长宽比、高度需满足条件
		return true;
	else
		return false;
}

void char_sort(vector <RotatedRect > & in_char ) //对字符区域进行排序
{
	vector <RotatedRect >  out_char;
	const int length = 7;           //7个字符
	int index[length] = {0,1,2,3,4,5,6};
	float centerX[length];
	for (int i=0;i < length ; ++ i)
	{
		centerX[i] = in_char[i].center.x;
	}

	for (int j=0;j <length;j++) {
		for (int i=length-2;i >= j;i--)
			if (centerX[i] > centerX[i+1])
			{
				float t=centerX[i];
				centerX[i]=centerX[i+1];
				centerX[i+1]=t;

				int tt = index[i];
				index[i] = index[i+1];
				index[i+1] = tt;
			}
	}

	for(int i=0;i<length ;i++)
		out_char.push_back(in_char[(index[i])]);

	in_char.clear();     //清空in_char
	in_char = out_char; //将排序好的字符区域向量重新赋值给in_char
}

void char_segment(const Mat & inputImg,vector <Mat>& dst_mat)//得到20*20的标准字符分割图像
{
	Mat img_threshold;

	threshold(inputImg ,img_threshold , 180,255 ,CV_THRESH_BINARY );//二值化
	//Mat element = getStructuringElement(MORPH_RECT ,Size(3 ,3));  //闭形态学的结构元素
	//morphologyEx(img_threshold ,img_threshold,CV_MOP_CLOSE,element);  //形态学处理

	//imshow ("img_thresho00ld",img_threshold);
	//waitKey();
	Mat img_contours;
	img_threshold.copyTo(img_contours);

	if (!clearLiuDing(img_contours))
    {
	   std::cout << "不是车牌" << endl;
	 }
	else
    {
	
		//imshow("img_cda",img_contours);
		//waitKey();
		Mat result2;
		inputImg.copyTo(result2);

		vector < vector <Point> > contours;
		findContours(img_contours ,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);

		vector< vector <Point> > ::iterator itc = contours.begin();
		vector<RotatedRect> char_rects;

		drawContours(result2,contours,-1, Scalar(0,255,255), 1); 
		while( itc != contours.end())
		{
			RotatedRect minArea = minAreaRect(Mat( *itc )); //返回每个轮廓的最小有界矩形区域
			Point2f vertices[4];
			minArea.points(vertices);

			if(!char_verifySizes(minArea))  //判断矩形轮廓是否符合要求
			{
				itc = contours.erase(itc);
			}
			else     
			{
				++itc; 
				char_rects.push_back(minArea);  	
			}  	
		}
		/*imshow("char1",char_rects[1]);
		imshow("char2",char_rects[2]);
		imshow("char3",char_rects[3]);
		imshow("char4",char_rects[4]);
		imshow("char5",char_rects[5]);
		imshow("char6",char_rects[6]);
		imshow("char7",char_rects[0]);
		waitKey();*/
	
	    char_sort(char_rects); //对字符排序

		vector <Mat> char_mat;

		for (int i = 0; i<char_rects.size() ;i++ )
		{
			char_mat.push_back(Mat(img_threshold,char_rects[i].boundingRect()));
		}
	
		//imshow("char_mat1",char_mat[0]);
		//imshow("char_mat2",char_mat[1]);
		//imshow("char_mat3",char_mat[2]);
		//imshow("char_mat4",char_mat[3]);
		//imshow("char_mat5",char_mat[4]);
		//imshow("char_mat6",char_mat[5]);
		//imshow("char_mat7",char_mat[6]);
		//waitKey();

		Mat train_mat(2,3,CV_32FC1);
		int length ;
		dst_mat.resize(7);
		Point2f srcTri[3];  
		Point2f dstTri[3];

		for (int i = 0; i==0;i++)
		{
			srcTri[0] = Point2f( 0,0 );  
			srcTri[1] = Point2f( char_mat[i].cols - 1, 0 );  
			srcTri[2] = Point2f( 0, char_mat[i].rows - 1 );
			length = char_mat[i].rows > char_mat[i].cols?char_mat[i].rows:char_mat[i].cols;
			dstTri[0] = Point2f( 0.0, 0.0 );  
			dstTri[1] = Point2f( length, 0.0 );  
			dstTri[2] = Point2f( 0.0, length ); 
			train_mat = getAffineTransform( srcTri, dstTri );
			dst_mat[i]=Mat::zeros(length,length,char_mat[i].type());		
			warpAffine(char_mat[i],dst_mat[i],train_mat,dst_mat[i].size(),INTER_LINEAR,BORDER_CONSTANT,Scalar(0));
			//resize(dst_mat[i],dst_mat[i],Size(20,20),0,0,CV_INTER_CUBIC);  //尺寸调整为20*20
			resize(dst_mat[i],dst_mat[i],Size(20,20));//每个字符归一化为20*20的字符
		}
		for (int i = 1; i< char_mat.size();++i)
		{
			srcTri[0] = Point2f( 0,0 );  
			srcTri[1] = Point2f( char_mat[i].cols - 1, 0 );  
			srcTri[2] = Point2f( 0, char_mat[i].rows - 1 );
			length = char_mat[i].rows > char_mat[i].cols?char_mat[i].rows:char_mat[i].cols;
			dstTri[0] = Point2f( 0.0, 0.0 );  
			dstTri[1] = Point2f( length, 0.0 );  
			dstTri[2] = Point2f( 0.0, length ); 
			train_mat = getAffineTransform( srcTri, dstTri );
			dst_mat[i]=Mat::zeros(length,length,char_mat[i].type());		
			warpAffine(char_mat[i],dst_mat[i],train_mat,dst_mat[i].size(),INTER_LINEAR,BORDER_CONSTANT,Scalar(0));
			//resize(dst_mat[i],dst_mat[i],Size(20,20),0,0,CV_INTER_CUBIC);  //尺寸调整为20*20
			resize(dst_mat[i],dst_mat[i],Size(20,20));//每个字符归一化为20*20的字符
		}
	}
}

void features(const Mat & in , Mat & out ,int sizeData)
{
	// 分别在水平方向和垂直方向上 创建累积直方图
	Mat vhist = projectHistogram(in , 1); //水平直方图
	Mat hhist = projectHistogram(in , 0);  //垂直直方图

	// 低分辨率图像
	// 低分辨率图像中的每一个像素都将被保存在特征矩阵中
	Mat lowData;
	resize(in , lowData ,Size(sizeData ,sizeData ));

	//特征矩阵的列数
	int numCols = vhist.cols + hhist.cols + lowData.cols * lowData.cols;
	out = Mat::zeros(1, numCols , CV_32F);

	// 向特征矩阵赋值
	int j = 0;
	for (int i =0 ;i<vhist.cols ; ++i)// 首先把水平方向累积直方图的值,存到特征矩阵中
	{
		out.at<float>(j) = vhist.at<float>(i);
		j++;
	}
	for (int i=0 ; i < hhist.cols ;++i)// 然后把竖直方向累积直方图的值,存到特征矩阵中
	{
		out.at<float>(j) = hhist.at<float>(i);
	}
	for(int x =0 ;x<lowData.rows ;++x)// 最后把低分辨率图像的像素值,存到特征矩阵中
	{
		for (int y =0 ;y < lowData.cols ;++ y)
		{
			out.at<float>(j) = (float)lowData.at<unsigned char>(x,y);
			j++;
		}
	}
}

void ann_train(CvANN_MLP &ann ,int numCharacters, int nlayers, string str)//http://blog.csdn.net/yiqiudream/article/details/51712497
{
	Mat trainData ,classes;
	FileStorage fs;
	fs.open(str, FileStorage::READ);//str是文件名字

	fs["TrainingData"] >>trainData;
	fs["classes"] >>classes;

	//CvANN_MLP bp;   
	//Set up BPNetwork's parameters  
	//CvANN_MLP_TrainParams params;  
	//params.train_method=CvANN_MLP_TrainParams::BACKPROP;  
	//params.bp_dw_scale=0.1;  
	//params.bp_moment_scale=0.1; 

	Mat layerSizes(1,3,CV_32SC1);
	layerSizes.at<int>( 0 ) = trainData.cols;
	layerSizes.at<int>( 1 ) = nlayers; //隐藏神经元数,可设为3
	layerSizes.at<int>( 2 ) = numCharacters; //样本类数为34
	//layerSizes.at<int>( 3 ) = numCharacters ;
	ann.create(layerSizes , CvANN_MLP::SIGMOID_SYM );  //初始化ann

	Mat trainClasses;
	trainClasses.create(trainData.rows , numCharacters ,CV_32FC1);
	for (int i =0;i< trainData.rows; i++)
	{
		for (int k=0 ; k< trainClasses.cols ; k++ )
		{
			if ( k == (int)classes.at<uchar> (i))
			{
				trainClasses.at<float>(i,k)  = 1 ;
			}
			else
				trainClasses.at<float>(i,k)  = 0;			
		}		
	}

	Mat weights(1 , trainData.rows , CV_32FC1 ,Scalar::all(1) );
	ann.train( trainData ,trainClasses , weights);
}

void svm_train(CvSVM & svmClassifier)
{
	FileStorage fs;

	fs.open("SVM.xml" , FileStorage::READ);
	Mat SVM_TrainningData;
	Mat SVM_Classes;	

	fs["TrainingData"] >>SVM_TrainningData;
	fs["classes"] >>SVM_Classes;
	CvSVMParams SVM_params;
	SVM_params.kernel_type = CvSVM::LINEAR;

	svmClassifier.train(SVM_TrainningData,SVM_Classes ,Mat(),Mat(),SVM_params); //SVM训练模型

	fs.release();
}

int main(int argc, char* argv[])
{
    Mat img_input= imread("./car.jpg");//加载图片
    if(img_input.empty())//如果读入图像失败
    {
        cout << "Can not load image" << endl;
        return -1;
    }

    Mat hsvImg ;
    cvtColor(img_input,hsvImg,CV_BGR2HSV);//RGB模型转换成HSV模型
    imwrite("car_hsv.jpg",hsvImg);//看下hsv效果

    vector <Mat> hsvSplit;
    split(hsvImg,hsvSplit);//将图像的各个通道分离
    equalizeHist(hsvSplit[2],hsvSplit[2]);//直方图均衡化,提高图像的质量
    merge(hsvSplit,hsvImg);//将分离的多个单通道合成一幅多通道图像
    imwrite("car_hsv1.jpg",hsvImg);//看下处理效果

    const int min_blue =100;//最小蓝车区域
    const int max_blue =240;//最大蓝车区域
    int avg_h = (min_blue+max_blue)/2;
    int channels = hsvImg.channels();
    int nRows = hsvImg.rows;
    //图像数据列需要考虑通道数的影响;
    int nCols = hsvImg.cols * channels;

    if (hsvImg.isContinuous())//连续存储的数据,按一行处理
    {
        nCols *= nRows;
        nRows = 1;
    }

    int i, j;
    unsigned char* p;
    const float  minref_sv = 64; //参考的S V的值
    const float max_sv = 255; // S V 的最大值

    for (i = 0; i < nRows; ++i)//根据蓝色在HSV在的区域每个通道的取值范围将此作为阈值,提取出图片中蓝色部分作为备选区域
    {
        p = hsvImg.ptr<uchar>(i);//有效提高了车牌和车色颜色在不相差较大的情况下的识别率
        for (j = 0; j < nCols; j += 3)//致命问题:蓝色的车和蓝色的牌照?
        {
            int H = int(p[j]); //0-180
            int S = int(p[j + 1]);  //0-255
            int V = int(p[j + 2]);  //0-255
            bool colorMatched = false;

            if (H > min_blue && H < max_blue)
            {
                int Hdiff = 0;
                float Hdiff_p = float(Hdiff) / 40;
                float min_sv = 0;

                if (H > avg_h)
                {
                    Hdiff = H - avg_h;
                }   
                else
                {
                    Hdiff = avg_h - H;
                }
                min_sv = minref_sv - minref_sv / 2 * (1 - Hdiff_p);
                if ((S > 70&& S < 255) &&(V > 70 && V < 255))
                    colorMatched = true;
            }
            if (colorMatched == true) 
            {
                p[j] = 0; p[j + 1] = 0; p[j + 2] = 255;
            }
            else 
            {
                p[j] = 0; p[j + 1] = 0; p[j + 2] = 0;
            }
        }
    }

    Mat src_grey;
    Mat img_threshold;
    vector<Mat> hsvSplit_done;

    split(hsvImg, hsvSplit_done);
    src_grey = hsvSplit_done[2];//提取黑色分量
    imwrite("car_hsvSplit.jpg",src_grey);//查看分离通道出来的车牌
    vector <RotatedRect>  rects;
    Mat element = getStructuringElement(MORPH_RECT ,Size(17 ,3));  //闭形态学的结构元素
    morphologyEx(src_grey ,img_threshold,CV_MOP_CLOSE,element); //闭运算,先膨胀后腐蚀,连通近邻区域(填补白色区域的间隙)
    morphologyEx(img_threshold,img_threshold,MORPH_OPEN,element);//形态学处理
    imwrite("car_morphology.jpg",img_threshold);//查看threshold

    vector< vector <Point> > contours;//寻找车牌区域的轮廓
    findContours(img_threshold ,contours,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);//只检测外轮廓。存储所以轮廓点
    //绘制轮廓
    /*for(int find=0; find < contours.size(); find++)
        drawContours(img_threshold, contours, find, Scalar(255), 2);
    imwrite("car_contours.jpg",img_threshold);//查看轮廓*/

    //对候选的轮廓进行进一步筛选
    vector< vector <Point> > ::iterator itc = contours.begin();
    while( itc != contours.end())
    {
        RotatedRect mr = minAreaRect(Mat( *itc )); //返回每个轮廓的最小有界矩形区域
        if(!verifySizes_closeImg(mr))  //判断矩形轮廓是否符合要求
        {
            itc = contours.erase(itc);
        }
        else     
        {

            rects.push_back(mr);
            ++itc;
        }      
    }
    vector <Mat> output_area;
    normal_area(img_input ,rects,output_area);  //获得144*33的候选车牌区域output_area
    imwrite("car_area.jpg",output_area[0]);//得到候选区域,这里可能会获得多个候选区域,最好使用svm训练一下

	//(二)添加如下:
	//CvSVM  svmClassifier;//为了运行速度,我就把这里注释掉了,这样会降低准确度
	//svm_train(svmClassifier);  //使用SVM对正负样本进行训练,为了运行速度,我就把这里注释掉了,这样会降低准确度
	vector<Mat> plates_svm;   //需要把候选车牌区域output_area图像中每个像素点作为一行特征向量,后进行预测

	for(int i=0;i< output_area.size(); ++i)//实际情况下应该加上SVM训练,我这里是学习测试
	{
		cout << "output " << i << endl;
		Mat img = output_area[i];
		Mat p = img.reshape(1,1);
		p.convertTo(p,CV_32FC1);
		//int response = (int)svmClassifier.predict( p );//为了运行速度,我就把这里注释掉了,这样会降低准确度
		//if (response == 1)//为了运行速度,我就把这里注释掉了,这样会降低准确度
			plates_svm.push_back(output_area[i]);    //保存预测结果
	}

	//从SVM预测获取车牌区域分割得到字符区域
	vector <Mat> char_seg;
	char_segment(plates_svm[0],char_seg);//对车牌区域中字符进行分割
	imwrite("char0.jpg",char_seg[0]);//显示七个字符
	imwrite("char1.jpg",char_seg[1]);
	imwrite("char2.jpg",char_seg[2]);
	imwrite("char3.jpg",char_seg[3]);
	imwrite("char4.jpg",char_seg[4]);
	imwrite("char5.jpg",char_seg[5]);
	imwrite("char6.jpg",char_seg[6]);
	
	//获得7个字符矩阵的相应特征矩阵
	vector <Mat> char_feature;
	char_feature.resize(7);
	for (int i =0;i<char_seg.size() ;++ i)
		features(char_seg[i], char_feature[i],5);
	
	//神经网络训练
	CvANN_MLP ann_classify;//对字母和数字
	ann_train(ann_classify,34,20,"ann_xml.xml");//输入层经元数(离线训练数据集的行数),隐藏层的神经元数,文件名字
	
	CvANN_MLP ann_classify1;//对第一个汉字进行分类建模
	ann_train(ann_classify1,3,20,"ann_xml_character.xml");
	//字符预测
	vector<int>  char_result;
	//classify(ann_classify,char_feature,char_result);

	char_result.resize(char_feature.size());
	for (int i=0;i<char_feature.size(); ++i)
	{
		if (i==0)//对汉字
	  	{
			Mat output(1 ,34, CV_32FC1); //1*34矩阵    
			//ann.predict(char_feature[i] ,output);
			ann_classify1.predict(char_feature[i],output);//对每个字符运用ANN.predict函数得出1*类别数的数据组(数据组中是记录这个字符跟每个类别的“相似度”)
			Point maxLoc;
			double maxVal;
			minMaxLoc(output , 0 ,&maxVal , 0 ,&maxLoc);//找出最大概率的类别
			char_result[i] =  maxLoc.x;
		}
		else//对字母和数字
		{
			Mat output(1 ,34, CV_32FC1); //1*34矩阵
			//ann.predict(char_feature[i] ,output);
			ann_classify.predict(char_feature[i],output);//预测
			Point maxLoc;
			double maxVal;
			minMaxLoc(output , 0 ,&maxVal , 0 ,&maxLoc);
			char_result[i] =  maxLoc.x;
		}
	}
	if(plates_svm.size() != 0)  
	{
		cout << "create a image" << endl;
    	imwrite("car_opencv_final.jpg",output_area[0]);     //正确预测的话,就只有一个结果plates_svm[0]	
	}
	else
	{
		std::cout<<"定位失败";
		return -1;
	}
	cout<<"该车牌后7位为:";
	char  s[] = {'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'};//现在添加了京
	cout<<'\n';	

	string chinese[]={"湘","鄂","粤","甘","贵","桂","黑","沪",
		"冀","津","京","吉","辽","鲁","蒙","闵","宁","青","琼",
		"陕","苏","晋","皖","湘","浙","豫","渝","粤","云"};
	for (int w=0;w<char_result.size(); w++)   //第一位是汉字,这里没实现对汉字的预测
	{     
		if (w==0)
		{
			cout<<chinese[char_result[w]];//可以对汉字京的识别
			cout<<'\t';
		}
		else
		{
			cout<< s[char_result[w]];
			cout<<'\t';
		}
	
	}
    return 0;
}
	

源代码和xml文件下载:https://download.csdn.net/download/guet_kite/10930196
代码大部分都是抄网上一篇文章的,文章地址我找不到了…
不过给出几个参考链接,可以看看里面的。
参考:
[1]:使用opencv的SVM和神经网络实现车牌识别
[2]:OpenCV自学笔记17. 基于SVM和神经网络的车牌识别
[3]:OpenCV实现车牌识别,OCR分割,ANN神经网络

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