ANN原理及响应函数设置如上所述；

ANN与其他分类算法有一个不同点是，构建和训练分开进行；

2.OpenCV实现代码

OCR::OCR(string trainFile){DEBUG=false;trained=false;saveSegments=false;//字符块的宽高charSize=20;//Read file storage.FileStorage fs;fs.open("OCR.xml", FileStorage::READ);//新建两个矩阵，用于存储训练数据和类别标签；Mat TrainingData;Mat Classes;fs["TrainingDataF15"] >> TrainingData;fs["classes"] >> Classes;//10层ANN,训练train(TrainingData, Classes, 10);}

2.2训练

void OCR::train(Mat TrainData, Mat classes, int nlayers){//矩阵，用于创建ANN；1行3列；训练数据的列数(特征属性)，ANN的层数，字符数（类别标签）；Mat layers(1,3,CV_32SC1);layers.at<int>(0)= TrainData.cols;layers.at<int>(1)= nlayers;layers.at<int>(2)= numCharacters;//使用sigmoid创建ANNann.create(layers, CvANN_MLP::SIGMOID_SYM, 1, 1);//Prepare trainClases//Create a mat with n trained data by m classesMat trainClasses;//TrainData.rows表示训练数据的数量；trainClasses.create( TrainData.rows, numCharacters, CV_32FC1 );for( int i = 0; i <  trainClasses.rows; i++ ){for( int k = 0; k < trainClasses.cols; k++ ){//If class of data i is same than a k class//trainClasses的第i个row存储第i个样本的标签类别，而此标签存储在class的第i个元素下；//class(numCharacters)-->trainClasses(TrainData.rows, numCharacters)矩阵转换；//(i,j)，遍历j；在样本i对应标签位置置为1；//比如第5个样本对应第3个类别，则设置trainClasses(5,3)=1；if( k == classes.at<int>(i) )trainClasses.at<float>(i,k) = 1;elsetrainClasses.at<float>(i,k) = 0;}}//所有的训练样本权重都为1；Mat weights( 1, TrainData.rows, CV_32FC1, Scalar::all(1) );if (DEBUG){//存储训练前的类别标签；FileStorage classBefore;classBefore.open("classBefore.xml", FileStorage::WRITE);classBefore << "before" << trainClasses;classBefore.release();}//Learn classifier，训练数据，类别标签（大矩阵形式），权重；//类别标签并不会变化，此API仅训练ANN;ann.train( TrainData, trainClasses, weights );if (DEBUG){//存储训练后的类别标签；FileStorage classAfter;classAfter.open("classAfter.xml", FileStorage::WRITE);classAfter << "after" << trainClasses;classAfter.release();}trained=true;
}

2.3 分类测试

ann.predict(f, output);

2.4 错误备注

2.4.1 训练类别需要使用整型 int

//set the class of train feature;vector<int> numberLabel;for (int colsIdx = 0; colsIdx<10; ++colsIdx)<span style="white-space:pre"> </span>numberLabel.push_back(colsIdx);Mat(numberLabel).copyTo(ftrClass);

for (int rowsIdx = 0, toFtrIdx = 0; rowsIdx < tempFtr.rows; ++rowsIdx)for (int colsIdx = 0; colsIdx < tempFtr.cols; ++colsIdx, ++toFtrIdx)ftrNumberTa.at<float>(nuIdex, toFtrIdx) = tempFtr.at<float>(rowsIdx, colsIdx);

MatName.at<dataType>(rowsIdx,colsIdx);

//cols即image的宽；rows即image的高Mat ftrNumberTa(cv::Size(ftrNumber_cols, ftrNumber_rows), CV_32F);

此文仅简单记载ANN识别在opencv中的使用流程；