/// <summary>
/// Recognize characters in given text images and returns character list </summary>
/// <param name="neuralNet"> </param>
/// <param name="image"> </param>
/// <param name="charDimension"> </param>
/// <returns> </returns>
public static List <char?> recognizeText(NeuralNetwork neuralNet, BufferedImage image, Dimension charDimension)
{
CharExtractor charExtractor = new CharExtractor(image);
List <BufferedImage> charImages = charExtractor.extractCharImagesToRecognize();
List <char?> characters = recognize(neuralNet, charImages, charDimension);
return(characters);
}
/// <summary>
/// Create training set
/// </summary>
/// <param name="imageWithChars"> </param>
/// <param name="chars"> </param>
/// <param name="scaleToDim"> </param>
/// <param name="trainingSetName"> </param>
public static DataSet createTrainingSet(string trainingSetName, BufferedImage imageWithChars, string chars, Dimension scaleToDim, List <string> imageLabels)
{
// convert chars from string to list
List <string> charList = Arrays.asList(chars.Split(" ", true)); // izgleda da ovo zeza...
// extract individual char images which will be used to create training set
CharExtractor charExtractor = new CharExtractor(imageWithChars);
Dictionary <string, BufferedImage> charImageMap = charExtractor.extractCharImagesToLearn(imageWithChars, charList, scaleToDim);
// prepare image labels (we need them to label output neurons...)
// ArrayList<String> imageLabels = new ArrayList<String>();
foreach (string imgName in charImageMap.Keys)
{
StringTokenizer st = new StringTokenizer(imgName, "._");
string imgLabel = st.nextToken();
if (!imageLabels.Contains(imgLabel)) // check for duplicates ...
{
imageLabels.Add(imgLabel);
}
}
imageLabels.Sort();
// get RGB image data - map chars and their their rgb data
IDictionary <string, FractionRgbData> imageRgbData = ImageUtilities.getFractionRgbDataForImages(charImageMap);
// also put junk all black and white image in training set (for black n whit emode)
BufferedImage allWhite = new BufferedImage(scaleToDim.Width, scaleToDim.Height, BufferedImage.TYPE_INT_RGB);
Graphics g = allWhite.Graphics;
g.Color = Color.WHITE;
g.fillRect(0, 0, allWhite.Width, allWhite.Height);
imageRgbData["allWhite"] = new FractionRgbData(allWhite);
// BufferedImage allBlack = new BufferedImage(charDimension.getWidth(), charDimension.getHeight(), BufferedImage.TYPE_INT_RGB);
// g = allBlack.getGraphics();
// g.setColor(Color.BLACK);
// g.fillRect(0, 0, allBlack.getWidth(), allBlack.getHeight());
// imageRgbData.put("allBlack", new FractionRgbData(allBlack));
// put junk images (all red, blue, green) to avoid errors (used for full color mode)
// BufferedImage allRed = new BufferedImage(charDimension.getWidth(), charDimension.getHeight(), BufferedImage.TYPE_INT_RGB);
// Graphics g = allRed.getGraphics();
// g.setColor(Color.RED);
// g.fillRect(0, 0, allRed.getWidth(), allRed.getHeight());
// imageRgbData.put("allRed", new FractionRgbData(allRed));
//
// BufferedImage allBlue = new BufferedImage(charDimension.getWidth(), charDimension.getHeight(), BufferedImage.TYPE_INT_RGB);
// g = allBlue.getGraphics();
// g.setColor(Color.BLUE);
// g.fillRect(0, 0, allBlue.getWidth(), allBlue.getHeight());
// imageRgbData.put("allBlue", new FractionRgbData(allBlue));
//
// BufferedImage allGreen = new BufferedImage(charDimension.getWidth(), charDimension.getHeight(), BufferedImage.TYPE_INT_RGB);
// g = allGreen.getGraphics();
// g.setColor(Color.GREEN);
// g.fillRect(0, 0, allGreen.getWidth(), allGreen.getHeight());
// imageRgbData.put("allGreen", new FractionRgbData(allGreen));
// create training set using image rgb data
DataSet dataSet = ImageRecognitionHelper.createBlackAndWhiteTrainingSet(imageLabels, imageRgbData);
//DataSet dataSet = ImageRecognitionHelper.createTrainingSet(this.imageLabels, imageRgbData);
dataSet.Label = trainingSetName;
return(dataSet);
}