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：
/**
*
*
* @author mjorcen
* @email [email protected]
* @dateTime Jun 19, 2014 3:42:16 PM
* @version 1
*/
public class PB {
static String path = "E:/test/code";
public static void main(String[] args) {
File file = new File(path);
for (String string : file.list()) {
File iFile = new File(path, string);
if (iFile.isFile()) {
pb2(string);
}
}
}
public static void pb2(String filename) {
try {
List<String> cmd = new LinkedList<String>();
cmd.add("tesseract");
cmd.add(filename);
cmd.add(filename);
ProcessBuilder pb = new ProcessBuilder(cmd);
pb.redirectErrorStream(true);
pb.directory(new File("E:/test/code"));
Process p = pb.start();
// 取得命令结果的输出流
InputStream fis = p.getInputStream();
// 用一个读输出流类去读
InputStreamReader isr = new InputStreamReader(fis, "gbk");
// 用缓冲器读行
BufferedReader br = new BufferedReader(isr);
String line = null;
// 直到读完为止
while ((line = br.readLine()) != null) {
// System.out.println(line);
}
// 取得结果的输出流
InputStream resultIs = new FileInputStream(new File(path, filename
+ ".txt"));
// 用一个读输出流类去读
InputStreamReader resultIsr = new InputStreamReader(resultIs, "gbk");
// 用缓冲器读行
BufferedReader resultBr = new BufferedReader(resultIsr);
line = null;
// 直到读完为止
while ((line = resultBr.readLine()) != null) {
System.out.print(line);
}
System.out.print(",");
} catch (Exception e) {
System.out.print(e.toString());
}
}
}
结果如下:
uHx7,IXQO,\1ZYP,ZVBO,3237,5SYQ~,,87YF,\8KDN,CGPC,\c\IG\N,F\Z TA,J 9pc,Lpza,NBGC,N QW8,onwz,ox XJ,\P9FM,P PR鈥楿,QRG\I\,,RAZ v\,504i,VGPH,VPCI,\\I\M I,鈥楳J1,Y6H9\,Y OGP,
对比第一张图片， 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， 能稍做优化，先看下效果图。
package cn.c.test3;
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("E:\\test\\code");
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;
}
}
Java OCR tesseract 图像智能字符识别技术 Java实现
时间： 2024-11-05 18:47:12