Java整合ORC识别验证码

首先需要下载orc软件，这里使用tesseract-orc软件好了
下载地址：点击下载
下载好了，就安装。
我们可以测试一下，在命令行输入tesseract 会出现以下情况

如果没有出现以下情况，需要手动的配置环境变量，方法如下：将安装目录添加到path中。

由于有的验证码有干扰点和颜色差，这里提供源码，将图片进行处理。

ClearImageHelper.java

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 {        cleanImage(new File("1.jpg"), "2.jpg");    }    // sfile是带处理的图像    // destDir是处理后保存的路径    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                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]);            }        }        // 去除噪点        for (int y = 0; y < h; y++) {            for (int x = 0; x < w; x++) {                int sum = 0;                if (isBlack(binaryBufferedImage.getRGB(x, y))) {                    sum = totalBlack(binaryBufferedImage, x, y);                }                // 如果非空白点的总数小于1，则认为是噪点，去除该噪点                if (sum <= 1) {                    binaryBufferedImage.setRGB(x, y, 0xffffff);                }            }        }        ImageIO.write(binaryBufferedImage, "jpg", new File(destDir));    }    // 得到该点周围的非空白点    public static int totalBlack(BufferedImage binaryBufferedImage, int x, int y) {        int sum = 0;        for (int i = x - 1; i <= x + 1; i++) {            if (i < 0 || i >= binaryBufferedImage.getWidth()) {                continue;            }            for (int j = y - 1; j <= y + 1; j++) {                if (j < 0 || j >= binaryBufferedImage.getHeight()) {                    continue;                }                try {                    if (isBlack(binaryBufferedImage.getRGB(i, j))) {                        sum++;                    }                } catch (Exception e) {                    continue;                }            }        }        return sum;    }    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;    }}

该算法有待改进，只能处理一部分验证码。运行以上代码后，将会生成2.jpg即修改过后的验证码图片。
我们接下来对2.jpg进行识别。

            Runtime runtime = Runtime.getRuntime();            runtime.exec("cmd /c tesseract 2.jpg 1");            Thread.sleep(2000);            BufferedReader bufferedReader = new BufferedReader(new FileReader("1.txt"));            String validate = bufferedReader.readLine();            bufferedReader.close();

这样字符串validate就是验证码的文字了。
原理就是Java代理模拟cmd命令，执行了tesseract 2.jpg 1 命令。
tesseract识别2.jpg，并将图片内容输出到1.txt文件中。再将1.txt文件读取到代码中。