本文主要是介绍常用的两种ORC 验证码 识别方法及实践感言，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
参考：
用Asprise的OCR包，处理验证码。
java ORC 图片中文识别
浅谈OCR之Tesseract
（原）测试 Tesseract-OCR 在windows平台过程记录
Java OCR 图像智能字符识别技术，可识别中文
来由，这几天想做坏事，从一个网站上批量查询东西，但是无奈每次查询都有验证码，所以就搜索到了以上几篇文章
基本介绍：
1、Asprise，是个收费的OCR软件，但是网络的力量是无穷的，可以下载到破解的
关于 Asprise的使用例子可以参考代码：
Asprise-OCR-Java示例代码
2、Tesseract，该技术是google的一个源码项目，出自HP(http://code.google.com/p/tesseract-ocr)
a、首先安装tesseract-ocr-setup-3.01-1.exe
b、安装好了以后你需要哪种语言或者类别的识别支持，到官网的downlist中去查找插件，并放置在安装目录的/tessdata文件夹下（如果需要中文支持，下载tesseract-ocr的中文包
chi_sim.traineddata.gz，解压缩之后复制到tesseract-ocr的安装目录/tessdata文件夹之下）见图
c、安装好以后，c++,java等等都可以进行tesseract的转换操作，我们就以命令行下为例
C:\Program Files\Tesseract-OCR>tesseract -help
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.
实例 tesseract xx.jpg output -l eng -psm 8
详解 tesseract即为安装目录下的tesseract.exe执行文件
xx.jpg即为你需要ORC解析的图片文件
output即为你需要将结果保存的文件名
-l eng 即为以英文字母模式进行解析
-psm 8即为以单行字母解析
关于Tesseract的JAVA中的使用说明可以参考代码：
tesseract安装包及JAVA代码实例
综合使用以后，发现这2者效果一般，识别率很低，
原因很简单，大多数网站的验证码都加入不同程度的噪音，以防止OCR软件的自动分析。
在Java OCR 图像智能字符识别技术，可识别中文 一文中谈到了进行一些图像去噪处理的简单方法，但是效果也一般，不过这的确提供了一些思路，只要有好的噪点处理方法肯定会提高OCR识别率。
package com.ocr;
import java.awt.Graphics2D;
import java.awt.color.ColorSpace;
import java.awt.geom.AffineTransform;
import java.awt.image.AffineTransformOp;
import java.awt.image.BufferedImage;
import java.awt.image.ColorConvertOp;
import java.awt.image.ColorModel;
import java.awt.image.MemoryImageSource;
import java.awt.image.PixelGrabber;
/**
*
* 图像过滤,增强OCR识别成功率
*
*/
public class ImageFilter {
private BufferedImage image;
private int iw, ih;
private int[] pixels;
public ImageFilter(BufferedImage image) {
this.image = image;
iw = image.getWidth();
ih = image.getHeight();
pixels = new int[iw * ih];
}
/** 图像二值化 */
public BufferedImage changeGrey() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 设定二值化的域值，默认值为100
int grey = 100;
// 对图像进行二值化处理，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 0; i < iw * ih; i++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i]);
if (cm.getRed(pixels[i]) > grey) {
red = 255;
} else {
red = 0;
}
if (cm.getGreen(pixels[i]) > grey) {
green = 255;
} else {
green = 0;
}
if (cm.getBlue(pixels[i]) > grey) {
blue = 255;
} else {
blue = 0;
}
pixels[i] = alpha << 24 | red << 16 | green << 8 | blue;
}
// 将数组中的象素产生一个图像
return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, pixels, 0, iw));
}
/** 提升清晰度,进行锐化 */
public BufferedImage sharp() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 象素的中间变量
int tempPixels[] = new int[iw * ih];
for (int i = 0; i < iw * ih; i++) {
tempPixels[i] = pixels[i];
}
// 对图像进行尖锐化处理，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 1; i < ih - 1; i++) {
for (int j = 1; j < iw - 1; j++) {
int alpha = cm.getAlpha(pixels[i * iw + j]);
// 对图像进行尖锐化
int red6 = cm.getRed(pixels[i * iw + j + 1]);
int red5 = cm.getRed(pixels[i * iw + j]);
int red8 = cm.getRed(pixels[(i + 1) * iw + j]);
int sharpRed = Math.abs(red6 - red5) + Math.abs(red8 - red5);
int green5 = cm.getGreen(pixels[i * iw + j]);
int green6 = cm.getGreen(pixels[i * iw + j + 1]);
int green8 = cm.getGreen(pixels[(i + 1) * iw + j]);
int sharpGreen = Math.abs(green6 - green5) + Math.abs(green8 - green5);
int blue5 = cm.getBlue(pixels[i * iw + j]);
int blue6 = cm.getBlue(pixels[i * iw + j + 1]);
int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]);
int sharpBlue = Math.abs(blue6 - blue5) + Math.abs(blue8 - blue5);
if (sharpRed > 255) {
sharpRed = 255;
}
if (sharpGreen > 255) {
sharpGreen = 255;
}
if (sharpBlue > 255) {
sharpBlue = 255;
}
tempPixels[i * iw + j] = alpha << 24 | sharpRed << 16 | sharpGreen << 8 | sharpBlue;
}
}
// 将数组中的象素产生一个图像
return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, tempPixels, 0, iw));
}
/** 中值滤波 */
public BufferedImage median() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 对图像进行中值滤波，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 1; i < ih - 1; i++) {
for (int j = 1; j < iw - 1; j++) {
int red, green, blue;
int alpha = cm.getAlpha(pixels[i * iw + j]);
// int red2 = cm.getRed(pixels[(i - 1) * iw + j]);
int red4 = cm.getRed(pixels[i * iw + j - 1]);
int red5 = cm.getRed(pixels[i * iw + j]);
int red6 = cm.getRed(pixels[i * iw + j + 1]);
// int red8 = cm.getRed(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (red4 >= red5) {
if (red5 >= red6) {
red = red5;
} else {
if (red4 >= red6) {
red = red6;
} else {
red = red4;
}
}
} else {
if (red4 > red6) {
red = red4;
} else {
if (red5 > red6) {
red = red6;
} else {
red = red5;
}
}
}
// int green2 = cm.getGreen(pixels[(i - 1) * iw + j]);
int green4 = cm.getGreen(pixels[i * iw + j - 1]);
int green5 = cm.getGreen(pixels[i * iw + j]);
int green6 = cm.getGreen(pixels[i * iw + j + 1]);
// int green8 = cm.getGreen(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (green4 >= green5) {
if (green5 >= green6) {
green = green5;
} else {
if (green4 >= green6) {
green = green6;
} else {
green = green4;
}
}
} else {
if (green4 > green6) {
green = green4;
} else {
if (green5 > green6) {
green = green6;
} else {
green = green5;
}
}
}
// int blue2 = cm.getBlue(pixels[(i - 1) * iw + j]);
int blue4 = cm.getBlue(pixels[i * iw + j - 1]);
int blue5 = cm.getBlue(pixels[i * iw + j]);
int blue6 = cm.getBlue(pixels[i * iw + j + 1]);
// int blue8 = cm.getBlue(pixels[(i + 1) * iw + j]);
// 水平方向进行中值滤波
if (blue4 >= blue5) {
if (blue5 >= blue6) {
blue = blue5;
} else {
if (blue4 >= blue6) {
blue = blue6;
} else {
blue = blue4;
}
}
} else {
if (blue4 > blue6) {
blue = blue4;
} else {
if (blue5 > blue6) {
blue = blue6;
} else {
blue = blue5;
}
}
}
pixels[i * iw + j] = alpha << 24 | red << 16 | green << 8 | blue;
}
}
// 将数组中的象素产生一个图像
return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, pixels, 0, iw));
}
/** 线性灰度变换 */
public BufferedImage lineGrey() {
PixelGrabber pg = new PixelGrabber(image.getSource(), 0, 0, iw, ih, pixels, 0, iw);
try {
pg.grabPixels();
} catch (InterruptedException e) {
e.printStackTrace();
}
// 对图像进行进行线性拉伸，Alpha值保持不变
ColorModel cm = ColorModel.getRGBdefault();
for (int i = 0; i < iw * ih; i++) {
int alpha = cm.getAlpha(pixels[i]);
int red = cm.getRed(pixels[i]);
int green = cm.getGreen(pixels[i]);
int blue = cm.getBlue(pixels[i]);
// 增加了图像的亮度
red = (int) (1.1 * red + 30);
green = (int) (1.1 * green + 30);
blue = (int) (1.1 * blue + 30);
if (red >= 255) {
red = 255;
}
if (green >= 255) {
green = 255;
}
if (blue >= 255) {
blue = 255;
}
pixels[i] = alpha << 24 | red << 16 | green << 8 | blue;
}
// 将数组中的象素产生一个图像
return ImageIOHelper.imageProducerToBufferedImage(new MemoryImageSource(iw, ih, pixels, 0, iw));
}
/** 转换为黑白灰度图 */
public BufferedImage grayFilter() {
ColorSpace cs = ColorSpace.getInstance(ColorSpace.CS_GRAY);
ColorConvertOp op = new ColorConvertOp(cs, null);
return op.filter(image, null);
}
/** 平滑缩放 */
public BufferedImage scaling(double s) {
AffineTransform tx = new AffineTransform();
tx.scale(s, s);
AffineTransformOp op = new AffineTransformOp(tx, AffineTransformOp.TYPE_BILINEAR);
return op.filter(image, null);
}
public BufferedImage scale(Float s) {
int srcW = image.getWidth();
int srcH = image.getHeight();
int newW = Math.round(srcW * s);
int newH = Math.round(srcH * s);
// 先做水平方向上的伸缩变换
BufferedImage tmp=new BufferedImage(newW, newH, image.getType());
Graphics2D g= tmp.createGraphics();
for (int x = 0; x < newW; x++) {
g.setClip(x, 0, 1, srcH);
// 按比例放缩
g.drawImage(image, x - x * srcW / newW, 0, null);
}
// 再做垂直方向上的伸缩变换
BufferedImage dst = new BufferedImage(newW, newH, image.getType());
g = dst.createGraphics();
for (int y = 0; y < newH; y++) {
g.setClip(0, y, newW, 1);
// 按比例放缩
g.drawImage(tmp, 0, y - y * srcH / newH, null);
}
return dst;
}
}
后记：
浅谈OCR之Onenote 2010
这个是另外一个OCR工具
20121115补充：
tesseract-ocr 识别码库训练方法 提高验证码识别率
这篇关于常用的两种ORC 验证码 识别方法及实践感言的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
