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

article/2025/7/16 9:28:15

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 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;}
}

好了，就到这里。如果这篇文章对你有用，赞一个吧~