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：

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。

——————————————————————-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~

原文地址:http://blog.csdn.net/lmj623565791/article/details/23960391