Java OCR tesseract 图像智能文字字符识别技术实例代码

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。

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; 
 } 
} 

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。