//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 parameters
//*******************************************************************************************************************************************
string imagePath = "C:/Users/Mihailo/Desktop/OCR/tekst.png"; //path to the image with letters (document) for recognition
string textPath = "C:/Users/Mihailo/Desktop/OCR/tekst.txt"; // path to the .txt file where the recognized text will be stored
string networkPath = "C:/Users/Mihailo/Desktop/OCR/network.nnet"; // locatoin of the trained network
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);
// Information about letters and text
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);
OCRTextRecognition recognition = new OCRTextRecognition(letterInfo, textInfo);
recognition.NetworkPath = networkPath;
recognition.recognize();
//if you want to save recognized text
// recognition.setRecognizedTextPath(textPath);
// recognition.saveText();
Console.WriteLine(recognition.RecognizedText);
}
//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);
}