コード例 #1
0
ファイル: OcrHelper.cs プロジェクト: starhash/Neuroph.NET
        /// <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);
        }
コード例 #2
0
ファイル: OcrHelper.cs プロジェクト: starhash/Neuroph.NET
        /// <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);
        }