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;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。
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*", "");
}
}
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~