Java OCR tesseract 图像智能字符识别技术 Java代码实现（二）

接着上一篇Java OCR tesseract 图像智能字符识别技术所说的，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：

[java] view
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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：

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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：

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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;

}