Java使用OpenCV和Tesseract-OCR实现银行卡图片处理与卡号识别

直接上代码，代码每一步都是解释与插图，一步步实现，如果不清楚opencv的环境如何搭建，可上网查或者参见我的前几篇博客，不多说了， java代码如下：

package com.zmx.opencvtest;import org.opencv.core.*;import org.opencv.imgcodecs.Imgcodecs;import org.opencv.imgproc.Imgproc;import javax.imageio.ImageIO;import java.awt.image.BufferedImage;import java.awt.image.DataBufferByte;import java.io.File;import java.io.IOException;/** * Created by zhangwenchao on 2017/9/27. */public class FirstOpenCVTest {    static {        //注意程序运行的时候需要在VM option添加该行 指明opencv的dll文件所在路径        //-Djava.library.path=$PROJECT_DIR$\opencv\x64        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);   //载入opencv all库    }    public static void main(String[] args) throws InterruptedException {        /**         * 1. 读取原始图像转换为OpenCV的Mat数据格式         */        Mat srcMat = Imgcodecs.imread("E:/srcImage.jpg");  //原始图像        /**         * 2. 强原始图像转化为灰度图像         */        Mat grayMat = new Mat(); //灰度图像        Imgproc.cvtColor(srcMat, grayMat, Imgproc.COLOR_RGB2GRAY);        BufferedImage grayImage =  toBufferedImage(grayMat);        saveJpgImage(grayImage,"E:/grayImage.jpg");        System.out.println("保存灰度图像！");        /**         * 3、对灰度图像进行二值化处理         */        Mat binaryMat = new Mat(grayMat.height(),grayMat.width(),CvType.CV_8UC1);        Imgproc.threshold(grayMat, binaryMat, 20, 255, Imgproc.THRESH_BINARY);        BufferedImage binaryImage =  toBufferedImage(binaryMat);        saveJpgImage(binaryImage,"E:/binaryImage.jpg");        System.out.println("保存二值化图像！");        /**         * 4、图像腐蚀---腐蚀后变得更加宽,粗.便于识别--使用3*3的图片去腐蚀         */        Mat destMat = new Mat(); //腐蚀后的图像        Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3, 3));        Imgproc.erode(binaryMat,destMat,element);        BufferedImage destImage =  toBufferedImage(destMat);        saveJpgImage(destImage,"E:/destImage.jpg");        System.out.println("保存腐蚀化后图像！");        /**         * 5 图片切割         */        //获取截图的范围--从第一行开始遍历,统计每一行的像素点值符合阈值的个数,再根据个数判断该点是否为边界        //判断该行的黑色像素点是否大于一定值（此处为150）,大于则留下,找到上边界,下边界后立即停止        int a =0, b=0, state = 0;        for (int y = 0; y < destMat.height(); y++)//行        {            int count = 0;            for (int x = 0; x < destMat.width(); x++) //列            {                //得到该行像素点的值                byte[] data = new byte[1];                destMat.get(y, x, data);                if (data[0] == 0)                    count = count + 1;            }            if (state == 0)//还未到有效行            {                if (count >= 150)//找到了有效行                {//有效行允许十个像素点的噪声                    a = y;                    state = 1;                }            }            else if (state == 1)            {                if (count <= 150)//找到了有效行                {//有效行允许十个像素点的噪声                    b = y;                    state = 2;                }            }        }        System.out.println("过滤下界"+Integer.toString(a));        System.out.println("过滤上界"+Integer.toString(b));        //参数,坐标X,坐标Y,截图宽度,截图长度        Rect rect = new Rect(0,a,destMat.width(),b - a);        Mat resMat = new Mat(destMat,rect);        BufferedImage resImage =  toBufferedImage(resMat);        saveJpgImage(resImage,"E:/resImage.jpg");        System.out.println("保存切割后图像！");        /**         * 识别-         */       /* try {            Process  pro = Runtime.getRuntime().exec(new String[]{"D:/Program Files (x86)/Tesseract-OCR/tesseract.exe", "E:/resImage.jpg","E:/result"});            pro.waitFor();        } catch (IOException e) {            e.printStackTrace();        }*/        try {            String result =  TesseractOCRUtil.recognizeText(new File("E:/resImage.jpg"));            System.out.println(result);        } catch (Exception e) {            e.printStackTrace();        }    }    /**     * 将Mat图像格式转化为 BufferedImage     * @param matrix  mat数据图像     * @return BufferedImage     */    private static BufferedImage toBufferedImage(Mat matrix) {        int type = BufferedImage.TYPE_BYTE_GRAY;        if (matrix.channels() > 1) {            type = BufferedImage.TYPE_3BYTE_BGR;        }        int bufferSize = matrix.channels() * matrix.cols() * matrix.rows();        byte[] buffer = new byte[bufferSize];        matrix.get(0, 0, buffer); // 获取所有的像素点        BufferedImage image = new BufferedImage(matrix.cols(), matrix.rows(), type);        final byte[] targetPixels = ((DataBufferByte)image.getRaster().getDataBuffer()).getData();        System.arraycopy(buffer, 0, targetPixels, 0, buffer.length);        return image;    }    /**     * 将BufferedImage内存图像保存为图像文件     * @param image BufferedImage     * @param filePath  文件名     */    private static void saveJpgImage(BufferedImage image, String filePath) {        try {            ImageIO.write(image, "jpg", new File(filePath));        } catch (Exception e) {            throw new RuntimeException(e);        }    }}

对于图片识别，我单独写了一个工具类，本文也有引用，java代码如下：

package com.zmx.opencvtest;/** * Created by zhangwenchao on 2017/9/28. */import java.io.BufferedReader;import java.io.File;import java.io.FileInputStream;import java.io.InputStreamReader;import java.util.ArrayList;import java.util.List;public class TesseractOCRUtil{    private static final String LANG_OPTION = "-l";    private static final String EOL = System.getProperty("line.separator");    /**     * @param imageFile     *            传入的图像文件     * @return 识别后的字符串     */    public static 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();        Process  pro = Runtime.getRuntime().exec(                         new String[]{                            "D:/Program Files (x86)/Tesseract-OCR/tesseract.exe",                            imageFile.getPath(),                            outputFile.getPath()}                         );       int w = pro.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*", "");    }    public static void main(String[] args) {        try {            String result =  recognizeText(new File("E:/resImage.jpg"));            System.out.println(result);        } catch (Exception e) {            e.printStackTrace();        }    }}

运行结果如下：

1、原始图像（网上不知哪位仁兄的银行卡）：

2、保存灰度图像！

3、保存二值化图像！

4、保存腐蚀化后图像！

5、获取的截取图像的上下边界
       过滤上界386
       过滤下界447

6、保存切割后图像！

7、识别的卡号：
         6228482298797273578

搞了一天的时间，总算大功告成，效果还不错！