Java实现验证码识别

java中识别验证码比较简单，使用的软件是tesseractocr，这个软件需要安装在本地中，傻瓜式安装（方便调用）。

github下载地址
https://github.com/tesseract-ocr/tessdata

博主是在官网下载的。

该软件默认的识别的是英文。如果需要识别中文，需要将中文的训练文本chi_sim.traineddata存放到C:\Program Files (x86)\Tesseract-OCR\tessdata。

简单的验证码识别直接调用 Tesseract的 doOCR(image)方法即可。如果验证码的噪点多并且有干扰线，这时候就需要对图像就行处理了。

图片处理大致思路：做灰度然后二值化然后去除干扰线。

话不多说上代码。

实现代码

public static void main(String[] args) { String url = "验证图片地址";//验证码保存地址String path= "C:\\Users\\Administrator\\Desktop\\1.jpg"; //下载验证码 downloadPicture(url,path);Demo demo= new Demo(); String code= demo.FindOCR(path,false);System.out.println(code);
}

下载验证码很简单就是用HTTPClient获取验证图片的链接然后下载就可以了。我这里只放一个下载的代码。至于获取连接的每个网站的请求也不一样就不放出了。

    private static void downloadPicture(String urlList,String path) {URL url = null;try {url = new URL(urlList);DataInputStream dataInputStream = new DataInputStream(url.openStream());FileOutputStream fileOutputStream = new FileOutputStream(new File(path));ByteArrayOutputStream output = new ByteArrayOutputStream();byte[] buffer = new byte[1024];int length;while ((length = dataInputStream.read(buffer)) > 0) {output.write(buffer, 0, length);}fileOutputStream.write(output.toByteArray());dataInputStream.close();fileOutputStream.close();} catch (MalformedURLException e) {e.printStackTrace();} catch (IOException e) {e.printStackTrace();}}

识别验证码的工具类

public  String FindVCode(String srcImg, boolean language) {try {File imgFile = new File(srcImg);if (!srcImg.exists()) {return "图片路径有误或不存在";}BufferedImage testImage = ImageIO.read(imgFile);Tesseract tesseract= new Tesseract();// 默认的图片库instance.setDatapath("/usr/local/share/tessdata/");if (language) {tesseract.setLanguage("chi_sim");}String vCode= null;// 下面是去图像优化的过程 不需要的可以不用 直接 vCode =instance.doOCR(testImage) ;BufferedImage cleanedImg = cleanLinesInImage(testImage);vCode= tesseract.doOCR(cleanedBufferedImage);return vCode;} catch (Exception e) {e.printStackTrace();return "未知错误";}}

图片处理过程

private BufferedImage cleanLinesInImage(BufferedImage image) throws IOException{BufferedImage bufferedImage = oriBufferedImage;int h = bufferedImage.getHeight();int w = bufferedImage.getWidth();for (int x = 0; x < width; x++) {for (int y = 0; y < height; y++) {boolean c = true;// 这个像素块上下左右是不是都是黑色的,如果是,这个像素当作黑色的int roundWhiteCount = 0;if (isBlackColor(bufferedImage , x + 1, y + 1)){roundWhiteCount++;}if (isBlackColor(bufferedImage , x + 1, y - 1)){roundWhiteCount++;}if (isBlackColor(bufferedImage , x - 1, y + 1)){roundWhiteCount++;}if (isBlackColor(bufferedImage , x - 1, y - 1)){roundWhiteCount++;}if (roundWhiteCount >= 4) {c = false;}if (!isBlackColor(bufferedImage , x, y) && c) {image.setRGB(x, y, 0xFFFFFFFF); //argb:AARRGGBB}}}//把不是纯白色的像素块变成黑色的，用来做判断条件for (int x = 0; x < width; x++) {for (int y = 0; y < height; y++) {// 不是纯白就填黑if ((bufferedImage .getRGB(x, y) & 0xFFFFFF) != (new Color(255, 255, 255).getRGB() & 0xFFFFFF)) {bufferedImage .setRGB(x, y, 0xFF000000);}}}// 二值化int threshold = ostu(gray, w, h);BufferedImage binaryBufferedImage= new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);for (int x = 0; x < w; x++){for (int y = 0; y < h; y++){if (gray[x][y] > threshold){gray[x][y] |= 0x00FFFF;} else{gray[x][y] &= 0xFF0000;}binaryBufferedImage.setRGB(x, y, gray[x][y]);}}cleanImage(binaryBufferedImage,h,w );return binaryBufferedImage;
}
private boolean isBlackColor(BufferedImage image, int x, int y) {// 检查这个像素块是不是边缘的if (x < 0 || y < 0 || x >= image.getWidth() || y >= image.getHeight()) {return false;}int pixel = image.getRGB(x, y);return// R(pixel & 0xFF0000) >> 16 < 30// G&& (pixel & 0xFF00) >> 8 < 30// B&& (pixel & 0xFF) < 30;
}
public void cleanImage(BufferedImage binaryBufferedImage,int h ,int w ){//去除干扰线条for(int y = 1; y < h-1; y++){for(int x = 1; x < w-1; x++){boolean flag = false ;if(isBlack(binaryBufferedImage.getRGB(x, y))){//左右均为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x-1, y)) && isWhite(binaryBufferedImage.getRGB(x+1, y))){flag = true;}//上下均为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x, y+1)) && isWhite(binaryBufferedImage.getRGB(x, y-1))){flag = true;}//斜上下为空时，去掉此点if(isWhite(binaryBufferedImage.getRGB(x-1, y+1)) && isWhite(binaryBufferedImage.getRGB(x+1, y-1))){flag = true;}if(isWhite(binaryBufferedImage.getRGB(x+1, y+1)) && isWhite(binaryBufferedImage.getRGB(x-1, y-1))){flag = true;}if(flag){binaryBufferedImage.setRGB(x,y,-1);}}}}}public Mat bufferedImageToMat(BufferedImage bi) {Mat mat = new Mat(bi.getHeight(), bi.getWidth(), CvType.CV_8UC1);byte[] white = new byte[] { (byte) 255 };byte[] black = new byte[] { (byte) 0 };for (int x=0; x<bi.getWidth(); x++) {for (int y=0; y<bi.getHeight(); y++) {if (bi.getRGB(x, y) == Color.BLACK.getRGB()) {mat.put(y, x, black);} else {mat.put(y, x, white);}}}return mat;}/*** Mat转换成BufferedImage** @param matrix*            要转换的Mat* @param fileExtension*            格式为 ".jpg", ".png", etc* @return*/public BufferedImage mat2BufImg (Mat matrix, String fileExtension) {// convert the matrix into a matrix of bytes appropriate for// this file extensionMatOfByte mob = new MatOfByte();Imgcodecs.imencode(fileExtension, matrix, mob);// convert the "matrix of bytes" into a byte arraybyte[] byteArray = mob.toArray();BufferedImage bufImage = null;try {InputStream in = new ByteArrayInputStream(byteArray);bufImage = ImageIO.read(in);} catch (Exception e) {e.printStackTrace();}return bufImage;}public boolean isBlack(int colorInt){Color color = new Color(colorInt);if (color.getRed() + color.getGreen() + color.getBlue() <= 300){return true;}return false;}public boolean isWhite(int colorInt){Color color = new Color(colorInt);if (color.getRed() + color.getGreen() + color.getBlue() > 300){return true;}return false;}public int isBlackOrWhite(int colorInt){if (getColorBright(colorInt) < 30 || getColorBright(colorInt) > 730){return 1;}return 0;}public int getColorBright(int colorInt){Color color = new Color(colorInt);return color.getRed() + color.getGreen() + color.getBlue();}public int ostu(int[][] gray, int w, int h){int[] histData = new int[w * h];// Calculate histogramfor (int x = 0; x < w; x++){for (int y = 0; y < h; y++){int red = 0xFF & gray[x][y];histData[red]++;}}// Total number of pixelsint total = w * h;float sum = 0;for (int t = 0; t < 256; t++)sum += t * histData[t];float sumB = 0;int wB = 0;int wF = 0;float varMax = 0;int threshold = 0;for (int t = 0; t < 256; t++){wB += histData[t]; // Weight Backgroundif (wB == 0)continue;wF = total - wB; // Weight Foregroundif (wF == 0)break;sumB += (float) (t * histData[t]);float mB = sumB / wB; // Mean Backgroundfloat mF = (sum - sumB) / wF; // Mean Foreground// Calculate Between Class Variancefloat varBetween = (float) wB * (float) wF * (mB - mF) * (mB - mF);// Check if new maximum foundif (varBetween > varMax){varMax = varBetween;threshold = t;}}return threshold;}

其中部分是借鉴网上的源代码。如果有编写不对，或者可以修改的更好的建议请指出。

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