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上传时间: 2014-03-27
详细说明： java 利用orc智能识别图像字符技术，虽然说不能够百分百识别，但还算有点用。package com.ocr; import java.awt.Graphics2D; import java.awt.color.ColorSpace; import java.awt.geom.AffineTransform; import java.awt.image.AffineTransformOp; import java.awt.image.BufferedImage; import java.awt.image.ColorConvertOp; import java.awt.image.ColorModel; import java.awt.image.MemoryImageSource; import java.awt.image.PixelGrabber; /** * * 图像过滤,增强OCR识别成功率 * */ public class ImageFilter { private BufferedImage image; private int iw, ih; private int[] pixels; public ImageFilter(BufferedImage image) { this.image = image; iw = image.getWidth(); ih = image.getHeight(); pixels = new int[iw * ih]; } /** 图像二值化 */ public BufferedImage changeGrey() { PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw); try { pg.grabPixels(); } catch (InterruptedException e) { e.printStackTrace(); } // 设定二值化的域值，默认值为100 int grey = 100; // 对图像进行二值化处理，Alpha值保持不变 ColorModel cm = ColorModel.getRGBdefault(); for (int i = 0; i < iw * ih; i++) { int red, green, blue; int alpha = cm.getAlpha(pixels[i]); if (cm.getRed(pixels[i]) > grey) { red = 255; } else { red = 0; } if (cm.getGreen(pixels[i]) > grey) { green = 255; } else { green = 0; } if (cm.getBlue(pixels[i]) > grey) { blue = 255; } else { blue = 0; } pixels[i] = alpha << 24 | red << 16 | green << 8 | blue; } // 将数组中的象素产生一个图像 return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, pixels, 0, iw)); } /** 提升清晰度,进行锐化 */ public BufferedImage sharp() { PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw); try { pg.grabPixels(); } catch (InterruptedException e) { e.printStackTrace(); } // 象素的中间变量 int tempPixels[] = new int[iw * ih]; for (int i = 0; i < iw * ih; i++) { tempPixels[i] = pixels[i]; } // 对图像进行尖锐化处理，Alpha值保持不变 ColorModel cm = ColorModel.getRGBdefault(); for (int i = 1; i < ih - 1; i++) { for (int j = 1; j < iw - 1; j++) { int alpha = cm.getAlpha(pixels[i * iw + j]); // 对图像进行尖锐化 int red6 = cm.getRed(pixels[i * iw + j + 1]); int red5 = cm.getRed(pixels[i * iw + j]); int red8 = cm.getRed(pixels[(i + 1) * iw + j]); int sharpRed = Math.abs(red6 - red5) + Math.abs(red8 - red5); int green5 = cm.getGreen(pixels[i * iw + j]); int green6 = cm.getGreen(pixels[i * iw + j + 1]); int green8 = cm.getGreen(pixels[(i + 1) * iw + j]); int sharpGreen = Math.abs(green6 - green5) + Math.abs(green8 - green5); int blue5 = cm.getBlue(pixels[i * iw + j]); int blue6 = cm.getBlue(pixels[i * iw + j + 1]); int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]); int sharpBlue = Math.abs(blue6 - blue5) + Math.abs(blue8 - blue5); if (sharpRed > 255) { sharpRed = 255; } if (sharpGreen > 255) { sharpGreen = 255; } if (sharpBlue > 255) { sharpBlue = 255; } tempPixels[i * iw + j] = alpha << 24 | sharpRed << 16 | sharpGreen << 8 | sharpBlue; } } // 将数组中的象素产生一个图像 return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, tempPixels, 0, iw)); } /** 中值滤波 */ public BufferedImage median() { PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw); try { pg.grabPixels(); } catch (InterruptedException e) { e.printStackTrace(); } // 对图像进行中值滤波，Alpha值保持不变 ColorModel cm = ColorModel.getRGBdefault(); for (int i = 1; i < ih - 1; i++) { for (int j = 1; j < iw - 1; j++) { int red, green, blue; int alpha = cm.getAlpha(pixels[i * iw + j]); // int red2 = cm.getRed(pixels[(i - 1) * iw + j]); int red4 = cm.getRed(pixels[i * iw + j - 1]); int red5 = cm.getRed(pixels[i * iw + j]); int red6 = cm.getRed(pixels[i * iw + j + 1]); // int red8 = cm.getRed(pixels[(i + 1) * iw + j]); // 水平方向进行中值滤波 if (red4 >= red5) { if (red5 >= red6) { red = red5; } else { if (red4 >= red6) { red = red6; } else { red = red4; } } } else { if (red4 > red6) { red = red4; } else { if (red5 > red6) { red = red6; } else { red = red5; } } } // int green2 = cm.getGreen(pixels[(i - 1) * iw + j]); int green4 = cm.getGreen(pixels[i * iw + j - 1]); int green5 = cm.getGreen(pixels[i * iw + j]); int green6 = cm.getGreen(pixels[i * iw + j + 1]); // int green8 = cm.getGreen(pixels[(i + 1) * iw + j]); // 水平方向进行中值滤波 if (green4 >= green5) { if (green5 >= green6) { green = green5; } else { if (green4 >= green6) { green = green6; } else { green = green4; } } } else { if (green4 > green6) { green = green4; } else { if (green5 > green6) { green = green6; } else { green = green5; } } } // int blue2 = cm.getBlue(pixels[(i - 1) * iw + j]); int blue4 = cm.getBlue(pixels[i * iw + j - 1]); int blue5 = cm.getBlue(pixels[i * iw + j]); int blue6 = cm.getBlue(pixels[i * iw + j + 1]); // int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]); // 水平方向进行中值滤波 if (blue4 >= blue5) { if (blue5 >= blue6) { blue = blue5; } else { if (blue4 >= blue6) { blue = blue6; } else { blue = blue4; } } } else { if (blue4 > blue6) { blue = blue4; } else { if (blue5 > blue6) { blue = blue6; } else { blue = blue5; } } } pixels[i * iw + j] = alpha << 24 | red << 16 | green << 8 | blue; } } // 将数组中的象素产生一个图像 return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, pixels, 0, iw)); } /** 线性灰度变换 */ public BufferedImage lineGrey() { PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw); try { pg.grabPixels(); } catch (InterruptedException e) { e.printStackTrace(); } // 对图像进行进行线性拉伸，Alpha值保持不变 ColorModel cm = ColorModel.getRGBdefault(); for (int i = 0; i < iw * ih; i++) { int alpha = cm.getAlpha(pixels[i]); int red = cm.getRed(pixels[i]); int green = cm.getGreen(pixels[i]); int blue = cm.getBlue(pixels[i]); // 增加了图像的亮度 red = (int) (1.1 * red + 30); green = (int) (1.1 * green + 30); blue = (int) (1.1 * blue + 30); if (red >= 255) { red = 255; } if (green >= 255) { green = 255; } if (blue >= 255) { blue = 255; } pixels[i] = alpha << 24 | red << 16 | green << 8 | blue; } // 将数组中的象素产生一个图像 return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, pixels, 0, iw)); } /** 转换为黑白灰度图 */ public BufferedImage grayFilter() { ColorSpace cs = ColorSpace.getInstance(ColorSpace.CS_GRAY); ColorConvertOp op = new ColorConvertOp(cs, null); return op.filter(image, null); } /** 平滑缩放 */ public BufferedImage scaling(double s) { AffineTransform tx = new AffineTransform(); tx.scale(s, s); AffineTransformOp op = new AffineTransformOp(tx, AffineTransformOp.TYPE_BILINEAR); return op.filter(image, null); } public BufferedImage scale(Float s) { int srcW = image.getWidth(); int srcH = image.getHeight(); int newW = Math.round(srcW * s); int newH = Math.round(srcH * s); // 先做水平方向上的伸缩变换 BufferedImage tmp=new BufferedImage(newW, newH, image.getType()); Graphics2D g= tmp.createGraphics(); for (int x = 0; x < newW; x++) { g.setClip(x, 0, 1, srcH); // 按比例放缩 g.drawImage(image, x - x * srcW / newW, 0, null); } // 再做垂直方向上的伸缩变换 BufferedImage dst = new BufferedImage(newW, newH, image.getType()); g = dst.createGraphics(); for (int y = 0; y < newH; y++) { g.setClip(0, y, newW, 1); // 按比例放缩 g.drawImage(tmp, 0, y - y * srcH / newH, null); } return dst; } } ...展开收缩
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