//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: public static void main(String[] args) throws java.io.IOException
public static void Main(string[] args)
{
// path to image directory
string imageDir = "/home/zoran/Downloads/MihailoHSLTest/trening";
// image names - used for output neuron labels
List <string> imageLabels = new ArrayList();
imageLabels.Add("bird");
imageLabels.Add("cat");
imageLabels.Add("dog");
// create dataset
IDictionary <string, FractionRgbData> map = ImageRecognitionHelper.getFractionRgbDataForDirectory(new File(imageDir), new Dimension(20, 20));
DataSet dataSet = ImageRecognitionHelper.createRGBTrainingSet(imageLabels, map);
// create neural network
List <int?> hiddenLayers = new List <int?>();
hiddenLayers.Add(12);
NeuralNetwork nnet = ImageRecognitionHelper.createNewNeuralNetwork("someNetworkName", new Dimension(20, 20), ColorMode.COLOR_RGB, imageLabels, hiddenLayers, TransferFunctionType.SIGMOID);
// set learning rule parameters
MomentumBackpropagation mb = (MomentumBackpropagation)nnet.LearningRule;
mb.LearningRate = 0.2;
mb.MaxError = 0.9;
mb.Momentum = 1;
// traiin network
Console.WriteLine("NNet start learning...");
nnet.learn(dataSet);
Console.WriteLine("NNet learned");
}
/// <summary>
/// Creates neural network for OCR, which contains OCR plugin. OCR plugin provides interface for character recognition. </summary>
/// <param name="label"> neural network label </param>
/// <param name="samplingResolution"> character size in pixels (all characters will be scaled to this dimensions during recognition) </param>
/// <param name="colorMode"> color mode used fr recognition </param>
/// <param name="characterLabels"> character labels for output neurons </param>
/// <param name="layersNeuronsCount"> number of neurons ih hidden layers </param>
/// <param name="transferFunctionType"> neurons transfer function type </param>
/// <returns> returns NeuralNetwork with the OCR plugin </returns>
public static NeuralNetwork createNewNeuralNetwork(string label, Dimension samplingResolution, ColorMode colorMode, List <string> characterLabels, List <int?> layersNeuronsCount, TransferFunctionType transferFunctionType)
{
NeuralNetwork neuralNetwork = ImageRecognitionHelper.createNewNeuralNetwork(label, samplingResolution, colorMode, characterLabels, layersNeuronsCount, transferFunctionType);
neuralNetwork.addPlugin(new OcrPlugin(samplingResolution, colorMode));
return(neuralNetwork);
}
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: public static void main(String[] args) throws java.io.IOException
public static void Main(string[] args)
{
// User input parameteres
//*******************************************************************************************************************************
string imagePath = "C:/Users/Mihailo/Desktop/OCR/slova.png"; //path to the image with letters *
string folderPath = "C:/Users/Mihailo/Desktop/OCR/ImagesDir/"; // loaction folder for storing segmented letters *
string textPath = "C:/Users/Mihailo/Desktop/OCR/slova.txt"; // path to the .txt file with text on the image *
string networkPath = "C:/Users/Mihailo/Desktop/OCR/network.nnet"; // location where the network will be stored *
int fontSize = 12; // fontSize, predicted by height of the letters, minimum font size is 12 pt *
int scanQuality = 300; // scan quality, minimum quality is 300 dpi *
//*******************************************************************************************************************************
BufferedImage image = ImageIO.read(new File(imagePath));
ImageFilterChain chain = new ImageFilterChain();
chain.addFilter(new GrayscaleFilter());
chain.addFilter(new OtsuBinarizeFilter());
BufferedImage binarizedImage = chain.processImage(image);
Letter letterInfo = new Letter(scanQuality, binarizedImage);
// letterInfo.recognizeDots(); // call this method only if you want to recognize dots and other litle characters, TODO
Text texTInfo = new Text(binarizedImage, letterInfo);
OCRTraining ocrTraining = new OCRTraining(letterInfo, texTInfo);
ocrTraining.FolderPath = folderPath;
ocrTraining.TrainingTextPath = textPath;
ocrTraining.prepareTrainingSet();
List <string> characterLabels = ocrTraining.CharacterLabels;
IDictionary <string, FractionRgbData> map = ImageRecognitionHelper.getFractionRgbDataForDirectory(new File(folderPath), new Dimension(20, 20));
DataSet dataSet = ImageRecognitionHelper.createBlackAndWhiteTrainingSet(characterLabels, map);
dataSet.FilePath = "C:/Users/Mihailo/Desktop/OCR/DataSet1.tset";
dataSet.save();
List <int?> hiddenLayers = new List <int?>();
hiddenLayers.Add(12);
NeuralNetwork nnet = ImageRecognitionHelper.createNewNeuralNetwork("someNetworkName", new Dimension(20, 20), ColorMode.BLACK_AND_WHITE, characterLabels, hiddenLayers, TransferFunctionType.SIGMOID);
BackPropagation bp = (BackPropagation)nnet.LearningRule;
bp.LearningRate = 0.3;
bp.MaxError = 0.1;
// MultiLayerPerceptron mlp = new MultiLayerPerceptron(12,13);
// mlp.setOutputNeurons(null);
Console.WriteLine("Start learning...");
nnet.learn(dataSet);
Console.WriteLine("NNet learned");
nnet.save(networkPath);
}