Java OCR tesseract 图像智能字符识别技术 Java代码实现

接着上一篇OCR所说的，上一篇给大家介绍了tesseract 在命令行的简单用法，当然了要继承到我们的程序中，还是需要代码实现的，下面给大家分享下java实现的例子。

拿代码扫描上面的图片，然后输出结果。主要思想就是利用Java调用系统任务。

下面是核心代码：

package com.zhy.test;    import java.io.BufferedReader;    import java.io.File;  import java.io.FileInputStream;  import java.io.InputStreamReader;  import java.util.ArrayList;  import java.util.List;    import org.jdesktop.swingx.util.OS;    public class OCRHelper  {   private final String LANG_OPTION = "-l";   private final String EOL = System.getProperty("line.separator");   /**    * 文件位置我防止在，项目同一路径    */   private String tessPath = new File("tesseract").getAbsolutePath();     /**    * @param imageFile    *            传入的图像文件    * @param imageFormat    *            传入的图像格式    * @return 识别后的字符串    */   public String recognizeText(File imageFile) throws Exception   {    /**     * 设置输出文件的保存的文件目录     */    File outputFile = new File(imageFile.getParentFile(), "output");      StringBuffer strB = new StringBuffer();    List<String> cmd = new ArrayList<String>();    if (OS.isWindowsXP())    {     cmd.add(tessPath + "\\tesseract");    } else if (OS.isLinux())    {     cmd.add("tesseract");    } else    {     cmd.add(tessPath + "\\tesseract");    }    cmd.add("");    cmd.add(outputFile.getName());    cmd.add(LANG_OPTION);  //  cmd.add("chi_sim");    cmd.add("eng");      ProcessBuilder pb = new ProcessBuilder();    /**     *Sets this process builder's working directory.     */    pb.directory(imageFile.getParentFile());    cmd.set(1, imageFile.getName());    pb.command(cmd);    pb.redirectErrorStream(true);    Process process = pb.start();    // tesseract.exe 1.jpg 1 -l chi_sim    // Runtime.getRuntime().exec("tesseract.exe 1.jpg 1 -l chi_sim");    /**     * the exit value of the process. By convention, 0 indicates normal     * termination.     */  //  System.out.println(cmd.toString());    int w = process.waitFor();    if (w == 0)// 0代表正常退出    {     BufferedReader in = new BufferedReader(new InputStreamReader(       new FileInputStream(outputFile.getAbsolutePath() + ".txt"),       "UTF-8"));     String str;       while ((str = in.readLine()) != null)     {      strB.append(str).append(EOL);     }     in.close();    } else    {     String msg;     switch (w)     {     case 1:      msg = "Errors accessing files. There may be spaces in your image's filename.";      break;     case 29:      msg = "Cannot recognize the image or its selected region.";      break;     case 31:      msg = "Unsupported image format.";      break;     default:      msg = "Errors occurred.";     }     throw new RuntimeException(msg);    }    new File(outputFile.getAbsolutePath() + ".txt").delete();    return strB.toString().replaceAll("\\s*", "");   }  }

代码很简单，中间那部分ProcessBuilder其实就类似Runtime.getRuntime().exec("tesseract.exe 1.jpg 1 -l chi_sim")，大家不习惯的可以使用Runtime。

测试代码：

package com.zhy.test;    import java.io.File;    public class Test  {   public static void main(String[] args)   {    try    {          File testDataDir = new File("testdata");     System.out.println(testDataDir.listFiles().length);     int i = 0 ;      for(File file :testDataDir.listFiles())     {      i++ ;      String recognizeText = new OCRHelper().recognizeText(file);      System.out.print(recognizeText+"\t");        if( i % 5  == 0 )      {       System.out.println();      }     }         } catch (Exception e)    {     e.printStackTrace();    }     }  }

输出结果：

对比第一张图片，是不是很完美~哈哈 ，当然了如果你只需要实现验证码的读写，那么上面就足够了。下面继续普及图像处理的知识。

-------------------------------------------------------------------我的分割线--------------------------------------------------------------------

当然了，有时候图片被扭曲或者模糊的很厉害，很不容易识别，所以下面我给大家介绍一个去噪的辅助类，绝对碉堡了，先看下效果图。

来张特写：

一个类，不依赖任何jar，把图像中的干扰线消灭了，是不是很给力，然后再拿这样的图片去识别，会不会效果更好呢，嘿嘿，大家自己实验~

代码：

package com.zhy.test;    import java.awt.Color;  import java.awt.image.BufferedImage;  import java.io.File;  import java.io.IOException;    import javax.imageio.ImageIO;    public class ClearImageHelper  {     public static void main(String[] args) throws IOException   {          File testDataDir = new File("testdata");    final String destDir = testDataDir.getAbsolutePath()+"/tmp";    for (File file : testDataDir.listFiles())    {     cleanImage(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 cleanImage(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 = 0; x < w; x++)    {     for (int y = 0; 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 >> 16) & 0xFF) * 1.1 + 30);      int g = (int) (((argb >> 8) & 0xFF) * 1.1 + 30);      int b = (int) (((argb >> 0) & 0xFF) * 1.1 + 30);      if (r >= 255)      {       r = 255;      }      if (g >= 255)      {       g = 255;      }      if (b >= 255)      {       b = 255;      }      gray[x][y] = (int) Math        .pow((Math.pow(r, 2.2) * 0.2973 + Math.pow(g, 2.2)          * 0.6274 + Math.pow(b, 2.2) * 0.0753), 1 / 2.2);     }    }      // 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]);     }    }      // 矩阵打印    for (int y = 0; y < h; y++)    {     for (int x = 0; 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() <= 300)    {     return true;    }    return false;   }     public static boolean isWhite(int colorInt)   {    Color color = new Color(colorInt);    if (color.getRed() + color.getGreen() + color.getBlue() > 300)    {     return true;    }    return false;   }     public static int isBlackOrWhite(int colorInt)   {    if (getColorBright(colorInt) < 30 || getColorBright(colorInt) > 730)    {     return 1;    }    return 0;   }     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 = 0; x < w; x++)    {     for (int y = 0; y < h; y++)     {      int red = 0xFF & gray[x][y];      histData[red]++;     }    }      // Total number of pixels    int 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 Background     if (wB == 0)      continue;       wF = total - wB; // Weight Foreground     if (wF == 0)      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;   }  }

好了，就到这里。如果这篇文章对你有用，赞一个吧~

来自： http://blog.csdn.net//lmj623565791/article/details/23960391