我正在努力从 BufferedImage 中快速有效地提取矩形中的单词。
例如，我有以下页面:(编辑!)扫描图像，因此它可能包含噪声、倾斜和失真。

如何在没有矩形的情况下提取以下图像: (编辑!)我可以使用 OpenCv 或任何其他库，但我对高级图像处理技术绝对陌生。

编辑

我使用了 karlphillip 建议的方法 here而且效果不错。
这是代码:

    package ro.ubbcluj.detection;

import java.awt.FlowLayout;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.List;

import javax.imageio.ImageIO;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.WindowConstants;

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;

public class RectangleDetection {

public static void main(String[] args) throws IOException {
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    Mat image = loadImage();
    Mat grayscale = convertToGrayscale(image);

    Mat treshold = tresholdImage(grayscale);
    List<MatOfPoint> contours = findContours(treshold);
    Mat contoursImage = fillCountours(contours, grayscale);
    Mat grayscaleWithContours = convertToGrayscale(contoursImage);
    Mat tresholdGrayscaleWithContours = tresholdImage(grayscaleWithContours);
    Mat eroded = erodeAndDilate(tresholdGrayscaleWithContours);
    List<MatOfPoint> squaresFound = findSquares(eroded);
    Mat squaresDrawn = Rectangle.drawSquares(grayscale, squaresFound);
    BufferedImage convertedImage = convertMatToBufferedImage(squaresDrawn);
    displayImage(convertedImage);
}

private static List<MatOfPoint> findSquares(Mat eroded) {
    return Rectangle.findSquares(eroded);
}

private static Mat erodeAndDilate(Mat input) {
    int erosion_type = Imgproc.MORPH_RECT;
    int erosion_size = 5;
    Mat result = new Mat();
    Mat element = Imgproc.getStructuringElement(erosion_type, new Size(2 * erosion_size + 1, 2 * erosion_size + 1));
    Imgproc.erode(input, result, element);
    Imgproc.dilate(result, result, element);
    return result;
}

private static Mat convertToGrayscale(Mat input) {
    Mat grayscale = new Mat();
    Imgproc.cvtColor(input, grayscale, Imgproc.COLOR_BGR2GRAY);
    return grayscale;
}

private static Mat fillCountours(List<MatOfPoint> contours, Mat image) {
    Mat result = image.clone();
    Imgproc.cvtColor(result, result, Imgproc.COLOR_GRAY2RGB);
    for (int i = 0; i < contours.size(); i++) {
        Imgproc.drawContours(result, contours, i, new Scalar(255, 0, 0), -1, 8, new Mat(), 0, new Point());
    }
    return result;
}

private static List<MatOfPoint> findContours(Mat image) {
    List<MatOfPoint> contours = new ArrayList<>();
    Mat hierarchy = new Mat();
    Imgproc.findContours(image, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_NONE);
    return contours;
}

private static Mat detectLinesHough(Mat img) {
    Mat lines = new Mat();
    int threshold = 80;
    int minLineLength = 10;
    int maxLineGap = 5;
    double rho = 0.4;
    Imgproc.HoughLinesP(img, lines, rho, Math.PI / 180, threshold, minLineLength, maxLineGap);
    Imgproc.cvtColor(img, img, Imgproc.COLOR_GRAY2RGB);
    System.out.println(lines.cols());
    for (int x = 0; x < lines.cols(); x++) {
        double[] vec = lines.get(0, x);
        double x1 = vec[0], y1 = vec[1], x2 = vec[2], y2 = vec[3];
        Point start = new Point(x1, y1);
        Point end = new Point(x2, y2);
        Core.line(lines, start, end, new Scalar(0, 255, 0), 3);
    }
    return img;
}

static BufferedImage convertMatToBufferedImage(Mat mat) throws IOException {
    MatOfByte matOfByte = new MatOfByte();
    Highgui.imencode(".jpg", mat, matOfByte);
    byte[] byteArray = matOfByte.toArray();
    InputStream in = new ByteArrayInputStream(byteArray);
    return ImageIO.read(in);

}

static void displayImage(BufferedImage image) {
    JFrame frame = new JFrame();
    frame.getContentPane().setLayout(new FlowLayout());
    frame.getContentPane().add(new JLabel(new ImageIcon(image)));
    frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
    frame.pack();
    frame.setVisible(true);

}

private static Mat tresholdImage(Mat img) {
    Mat treshold = new Mat();
    Imgproc.threshold(img, treshold, 225, 255, Imgproc.THRESH_BINARY_INV);
    return treshold;
}

private static Mat tresholdImage2(Mat img) {
    Mat treshold = new Mat();
    Imgproc.threshold(img, treshold, -1, 255, Imgproc.THRESH_BINARY_INV + Imgproc.THRESH_OTSU);
    return treshold;
}

private static Mat loadImage() {
    return Highgui
            .imread("E:\\Programs\\Eclipse Workspace\\LicentaWorkspace\\OpenCvRectangleDetection\\src\\img\\form3.jpg");
}

和 Rectangle 类

    package ro.ubbcluj.detection;

import java.awt.image.BufferedImage;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;

public class Rectangle {
static List<MatOfPoint> findSquares(Mat input) {
    Mat pyr = new Mat();
    Mat timg = new Mat();

    // Down-scale and up-scale the image to filter out small noises
    Imgproc.pyrDown(input, pyr, new Size(input.cols() / 2, input.rows() / 2));
    Imgproc.pyrUp(pyr, timg, input.size());
    // Apply Canny with a threshold of 50
    Imgproc.Canny(timg, timg, 0, 50, 5, true);

    // Dilate canny output to remove potential holes between edge segments
    Imgproc.dilate(timg, timg, new Mat(), new Point(-1, -1), 1);

    // find contours and store them all as a list
    Mat hierarchy = new Mat();
    List<MatOfPoint> contours = new ArrayList<>();
    Imgproc.findContours(timg, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
    List<MatOfPoint> squaresResult = new ArrayList<MatOfPoint>();
    for (int i = 0; i < contours.size(); i++) {

        // Approximate contour with accuracy proportional to the contour
        // perimeter
        MatOfPoint2f contour = new MatOfPoint2f(contours.get(i).toArray());
        MatOfPoint2f approx = new MatOfPoint2f();
        double epsilon = Imgproc.arcLength(contour, true) * 0.02;
        boolean closed = true;
        Imgproc.approxPolyDP(contour, approx, epsilon, closed);
        List<Point> approxCurveList = approx.toList();

        // Square contours should have 4 vertices after approximation
        // relatively large area (to filter out noisy contours)
        // and be convex.
        // Note: absolute value of an area is used because
        // area may be positive or negative - in accordance with the
        // contour orientation
        boolean aproxSize = approx.rows() == 4;
        boolean largeArea = Math.abs(Imgproc.contourArea(approx)) > 200;
        boolean isConvex = Imgproc.isContourConvex(new MatOfPoint(approx.toArray()));
        if (aproxSize && largeArea && isConvex) {
            double maxCosine = 0;
            for (int j = 2; j < 5; j++) {
                // Find the maximum cosine of the angle between joint edges
                double cosine = Math.abs(getAngle(approxCurveList.get(j % 4), approxCurveList.get(j - 2),
                        approxCurveList.get(j - 1)));
                maxCosine = Math.max(maxCosine, cosine);
            }
            // If cosines of all angles are small
            // (all angles are ~90 degree) then write quandrange
            // vertices to resultant sequence
            if (maxCosine < 0.3) {
                Point[] points = approx.toArray();
                squaresResult.add(new MatOfPoint(points));
            }
        }
    }
    return squaresResult;
}

// angle: helper function.
// Finds a cosine of angle between vectors from pt0->pt1 and from pt0->pt2.
private static double getAngle(Point point1, Point point2, Point point0) {
    double dx1 = point1.x - point0.x;
    double dy1 = point1.y - point0.y;
    double dx2 = point2.x - point0.x;
    double dy2 = point2.y - point0.y;
    return (dx1 * dx2 + dy1 * dy2) / Math.sqrt((dx1 * dx1 + dy1 * dy1) * (dx2 * dx2 + dy2 * dy2) + 1e-10);
}

public static Mat drawSquares(Mat image, List<MatOfPoint> squares) {
    Mat result = new Mat();
    Imgproc.cvtColor(image, result, Imgproc.COLOR_GRAY2RGB);
    int thickness = 2;
    Core.polylines(result, squares, false, new Scalar(0, 255, 0), thickness);
    return result;
}
}

结果示例:

...不过，它对较小的图像效果不是很好:

也许可以建议一些改进？或者如果我有一批图像要处理，如何使算法更快？

最佳答案

我使用 opencv 在 c++ 中完成了以下程序(我不熟悉 java+opencv)。我已经包含了您提供的两个示例图像的输出。对于其他一些图像，您可能需要在轮廓过滤部分调整阈值。

#include "stdafx.h"

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int _tmain(int argc, _TCHAR* argv[])
{
    // load image as grayscale
    Mat im = imread(INPUT_FILE, CV_LOAD_IMAGE_GRAYSCALE);

    Mat morph;
    // morphological closing with a column filter : retain only large vertical edges
    Mat morphKernelV = getStructuringElement(MORPH_RECT, Size(1, 7));
    morphologyEx(im, morph, MORPH_CLOSE, morphKernelV);

    Mat bwV;
    // binarize: will contain only large vertical edges
    threshold(morph, bwV, 0, 255.0, CV_THRESH_BINARY | CV_THRESH_OTSU);

    // morphological closing with a row filter : retain only large horizontal edges
    Mat morphKernelH = getStructuringElement(MORPH_RECT, Size(7, 1));
    morphologyEx(im, morph, MORPH_CLOSE, morphKernelH);

    Mat bwH;
    // binarize: will contain only large horizontal edges
    threshold(morph, bwH, 0, 255.0, CV_THRESH_BINARY | CV_THRESH_OTSU);

    // combine the virtical and horizontal edges
    Mat bw = bwV & bwH;
    threshold(bw, bw, 128.0, 255.0, CV_THRESH_BINARY_INV);

    // just for illustration
    Mat rgb;
    cvtColor(im, rgb, CV_GRAY2BGR);

    // find contours
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(bw, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
    // filter contours by area to obtain boxes
    double areaThL = bw.rows * .04 * bw.cols * .06;
    double areaThH = bw.rows * .7 * bw.cols * .7;
    double area = 0;
    for(int idx = 0; idx >= 0; idx = hierarchy[idx][0])
    {
        area = contourArea(contours[idx]); 
        if (area > areaThL && area < areaThH)
        {
            drawContours(rgb, contours, idx, Scalar(0, 0, 255), 2, 8, hierarchy);
            // take bounding rectangle. better to use filled countour as a mask
            // to extract the rectangle because then you won't get any stray elements
            Rect rect = boundingRect(contours[idx]);
            cout << "rect: (" << rect.x << ", " << rect.y << ") " << rect.width << " x " << rect.height << endl;
            Mat imRect(im, rect);
        }
    }

    return 0;
}

第一张图片的结果:

第二张图片的结果:

关于java - 从文本中提取矩形中的单词，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/23289387/