最近闲来无事,想研究下图片识别,经过一番搜索,决定研究研究tesseract
首先是一些基础概念
- OCR(Optical Character Recognition):光学字符识别,是指对图片文件中的文字进行分析识别,获取的过程。
- Tesseract:开源的OCR识别引擎,初期Tesseract引擎由HP实验室研发,后来贡献给了开源软件业,后经由Google进行改进,消除bug,优化,重新发布。当前版本为3.01.
首先介绍windows中使用命令行来使用Tesseract
- 下载安装Tesseract-OCR引擎(3.0版本+才支持中文识别) 下载链接 tesseract-ocr-setup-3.02.02
- 下载完后进行安装,默认情况下安装程序会给你配置系统环境变量,以指向安装目录(之后可以通过DOS界面在任意目录运行tesseract)。安装完成后目录如下:
tessdata 目录存放的是语言字库文件,和在命令行界面中可能用到的参数所对应的文件. 这个安装程序默认包含了英文字库。
如果想能识别中文,可以到https://github.com/tesseract-ocr/tessdata下载对应的语言的字库文件.
- 使用Tessract-OCR引擎识别验证码
打开DOS界面,输入tesseract,出现下图证明成功 - 我准备了一张验证码code.png放在D盘根目录下的test文件夹下,上图:
查看result.txt中解析结果
解析成功 - 那对中文的解析如何,准备一张中文图片
,在上述github上下载对应的中文语言库文件,键入命令 tesseract zhongwen.png result -l chi_sim,显示指定语言为中文,然后会发现控制台输出read_params_file: parameter not found: allow_blob_division,这是由于我们使用的版本是3.02,而github最新的资源版本已经是3.04了,这边我们下载个3.02版本下的中文简体字体就可以了,传送门替换后执行结果为
识别准确率还是可以的,毕竟我们还可以通过训练提高识别的准群率 - 附录:
Usage:tesseract imagename outputbase [-l lang] [-psm pagesegmode] [configfile...]
pagesegmode values are:
0 = Orientation and script detection (OSD) only.
1 = Automatic page segmentation with OSD.
2 = Automatic page segmentation, but no OSD, or OCR
3 = Fully automatic page segmentation, but no OSD. (Default)
4 = Assume a single column of text of variable sizes.
5 = Assume a single uniform block of vertically aligned text.
6 = Assume a single uniform block of text.
7 = Treat the image as a single text line.
8 = Treat the image as a single word.
9 = Treat the image as a single word in a circle.
10 = Treat the image as a single character.
-l lang and/or -psm pagesegmode must occur before anyconfigfile.
tesseract imagename outputbase [-l lang] [-psm pagesegmode] [configfile...]
tesseract 图片名 输出文件名 -l 字库文件 -psm pagesegmode 配置文件
例如:
tesseract code.jpg result -l chi_sim -psm 7 nobatch
-l chi_sim 表示用简体中文字库(需要下载中文字库文件,解压后,存放到tessdata目录下去,字库文件扩展名为 .raineddata 简体中文字库文件名为: chi_sim.traineddata)
-psm 7 表示告诉tesseract code.jpg图片是一行文本 这个参数可以减少识别错误率. 默认为 3
configfile 参数值为tessdata\configs 和 tessdata\tessconfigs 目录下的文件名 - 多数情况下,我们需要在程序中动态的调用,这边我们以java为例,演示如何在java中动态调用tesseract进行图像识别,我们在代码中识别下述的图片
下面是核心代码
package com.layou;
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");
private String tessPath = "D://Program Files (x86)//Tesseract-OCR";
/**
* @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*", "");
}
}
package com.layou;
import java.io.File;
public class Test {
public static void main(String[] args) {
try {
File testDataDir = new File("D://test");
System.out.println(testDataDir.listFiles().length);
int i = 0;
for (int j=0; j<24; j++) {
i++;
String recognizeText = new OCRHelper().recognizeText(new File("D://test/code" + j + ".jpg"));
System.out.print(recognizeText + "\t");
if (i % 8 == 0) {
System.out.println();
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
- 运行结果如下
运行结果还是相当令人满意的
-
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。
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~
package com.layou;
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("d://test");
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;
}
}
好拉,初步研究就到这边了,后期项目如果使用到的话,在深入研究吧,java部分参考