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~

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