java-BufferedImage 图片验证码去除干扰线的方法（ 用于OCR tesseract图像智能字符识别）

最近工作需要做了一下图片验证码自动识别的功能。但是网上对于初始图片的处理方法有去噪点、灰度化等，唯独难搜到去除干扰线的方法。于是根据网上搜来的代码，自己尝试写了一段，亲测有效，可以比较干净地去除干扰线，提高OCR识别的准确率。

• 以下代码除“去除干扰线条“”一小段为原创，其他均为网上搜寻所得，但是很抱歉我忘记了来源网址，以后如果能找到再补上。在此先谢过慷慨分享原始代码的前辈！

demo如下：

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;


import javax.imageio.ImageIO;


public class CopyOfCleanLines {

      public static void main(String[] args) throws IOException  
        {    
            File testDataDir = new File("imgWithLines");  
            final String destDir = testDataDir.getAbsolutePath()+"/tmp";  
            for (File file : testDataDir.listFiles())  
            {  
                cleanLinesInImage(file, destDir);  
                cleanLinesInImage(file, destDir); 
                cleanLinesInImage(file, destDir);
            }  
        }  

      /** 
         *  
         * @param sfile 
         *            需要去噪的图像 
         * @param destDir 
         *            去噪后的图像保存地址 
         * @throws IOException 
         */  
        public static void cleanLinesInImage(File sfile, String destDir)  throws IOException{  
            File destF = new File(destDir);  
            if (!destF.exists())  
            {  
                destF.mkdirs();  
            }  

            BufferedImage bufferedImage = ImageIO.read(sfile);  
            int h = bufferedImage.getHeight();  
            int w = bufferedImage.getWidth();  

            // 灰度化  
            int[][] gray = new int[w][h];  
            for (int x = ; x < w; x++)  
            {  
                for (int y = ; y < h; y++)  
                {  
                    int argb = bufferedImage.getRGB(x, y);  
                    // 图像加亮（调整亮度识别率非常高）  
                    int r = (int) (((argb >> ) & ) *  + );  
                    int g = (int) (((argb >> ) & ) *  + );  
                    int b = (int) (((argb >> ) & ) *  + );  
                    if (r >= )  
                    {  
                        r = ;  
                    }  
                    if (g >= )  
                    {  
                        g = ;  
                    }  
                    if (b >= )  
                    {  
                        b = ;  
                    }  
                    gray[x][y] = (int) Math  
                            .pow((Math.pow(r, ) *  + Math.pow(g, )  
                                    *  + Math.pow(b, ) * ),  / );  
                }  
            }  

            // 二值化  
            int threshold = ostu(gray, w, h);  
            BufferedImage binaryBufferedImage = new BufferedImage(w, h, BufferedImage.TYPE_BYTE_BINARY);  
            for (int x = ; x < w; x++)  
            {  
                for (int y = ; y < h; y++)  
                {  
                    if (gray[x][y] > threshold)  
                    {  
                        gray[x][y] |= ;  
                    } else  
                    {  
                        gray[x][y] &= ;  
                    }  
                    binaryBufferedImage.setRGB(x, y, gray[x][y]);  
                }  
            }  

            //去除干扰线条
            for(int y = ; y < h-; y++){
                for(int x = ; x < w-; 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-, y)) && isWhite(binaryBufferedImage.getRGB(x+, y))){
                            flag = true;
                        }
                        //上下均为空时，去掉此点
                        if(isWhite(binaryBufferedImage.getRGB(x, y+)) && isWhite(binaryBufferedImage.getRGB(x, y-))){
                            flag = true;
                        }
                        //斜上下为空时，去掉此点
                        if(isWhite(binaryBufferedImage.getRGB(x-, y+)) && isWhite(binaryBufferedImage.getRGB(x+, y-))){
                            flag = true;
                        }
                        if(isWhite(binaryBufferedImage.getRGB(x+, y+)) && isWhite(binaryBufferedImage.getRGB(x-, y-))){
                            flag = true;
                        } 
                        if(flag){
                            binaryBufferedImage.setRGB(x,y,-);                     
                        }
                    }
                }
            }


            // 矩阵打印  
            for (int y = ; y < h; y++)  
            {  
                for (int x = ; x < w; x++)  
                {  
                    if (isBlack(binaryBufferedImage.getRGB(x, y)))  
                    {  
                        System.out.print("*");  
                    } else  
                    {  
                        System.out.print(" ");  
                    }  
                }  
                System.out.println();  
            }  

            ImageIO.write(binaryBufferedImage, "jpg", new File(destDir, sfile  
                    .getName()));  
        }  

        public static boolean isBlack(int colorInt)  
        {  
            Color color = new Color(colorInt);  
            if (color.getRed() + color.getGreen() + color.getBlue() <= )  
            {  
                return true;  
            }  
            return false;  
        }  

        public static boolean isWhite(int colorInt)  
        {  
            Color color = new Color(colorInt);  
            if (color.getRed() + color.getGreen() + color.getBlue() > )  
            {  
                return true;  
            }  
            return false;  
        }  

        public static int isBlackOrWhite(int colorInt)  
        {  
            if (getColorBright(colorInt) <  || getColorBright(colorInt) > )  
            {  
                return ;  
            }  
            return ;  
        }  

        public static int getColorBright(int colorInt)  
        {  
            Color color = new Color(colorInt);  
            return color.getRed() + color.getGreen() + color.getBlue();  
        }  

        public static int ostu(int[][] gray, int w, int h)  
        {  
            int[] histData = new int[w * h];  
            // Calculate histogram  
            for (int x = ; x < w; x++)  
            {  
                for (int y = ; y < h; y++)  
                {  
                    int red =  & gray[x][y];  
                    histData[red]++;  
                }  
            }  

            // Total number of pixels  
            int total = w * h;  

            float sum = ;  
            for (int t = ; t < ; t++)  
                sum += t * histData[t];  

            float sumB = ;  
            int wB = ;  
            int wF = ;  

            float varMax = ;  
            int threshold = ;  

            for (int t = ; t < ; t++)  
            {  
                wB += histData[t]; // Weight Background  
                if (wB == )  
                    continue;  

                wF = total - wB; // Weight Foreground  
                if (wF == )  
                    break;  

                sumB += (float) (t * histData[t]);  

                float mB = sumB / wB; // Mean Background  
                float mF = (sum - sumB) / wF; // Mean Foreground  

                // Calculate Between Class Variance  
                float varBetween = (float) wB * (float) wF * (mB - mF) * (mB - mF);  

                // Check if new maximum found  
                if (varBetween > varMax)  
                {  
                    varMax = varBetween;  
                    threshold = t;  
                }  
            }  

            return threshold;  
        }  
}