提取文本OpenCV [英] Extracting text OpenCV

问题描述

我试图找到图片中的文本边框，并且目前正在使用此方法：

I am trying to find the bounding boxes of text in an image and am currently using this approach:

// calculate the local variances of the grayscale image
Mat t_mean, t_mean_2;
Mat grayF;
outImg_gray.convertTo(grayF, CV_32F);
int winSize = 35;
blur(grayF, t_mean, cv::Size(winSize,winSize));
blur(grayF.mul(grayF), t_mean_2, cv::Size(winSize,winSize));
Mat varMat = t_mean_2 - t_mean.mul(t_mean);
varMat.convertTo(varMat, CV_8U);

// threshold the high variance regions
Mat varMatRegions = varMat > 100;

当给出如下图像时：

varMatRegions 我得到这张图片：

正如你所看到的，它有点结合左边的文本块和卡的标题，卡这种方法工作伟大，但在更忙的卡，它可以导致问题。

As you can see it somewhat combines the left block of text with the header of the card, for most cards this method works great but on busier cards it can cause problems.

这些轮廓连接不好的原因是轮廓的边框几乎占用了整个卡。

The reason it is bad for those contours to connect is that it makes the bounding box of the contour nearly take up the entire card.

任何人都可以建议一种不同的方式，我可以找到文本，以确保正确检测的文本？

Can anyone suggest a different way I can find the text to ensure proper detection of text?

200点谁可以找到

推荐答案

您可以通过查找边缘元素来检测文本（源自LPD）：

You can detect text by finding close edge elements (inspired from a LPD):

#include "opencv2/opencv.hpp"

std::vector<cv::Rect> detectLetters(cv::Mat img)
{
    std::vector<cv::Rect> boundRect;
    cv::Mat img_gray, img_sobel, img_threshold, element;
    cvtColor(img, img_gray, CV_BGR2GRAY);
    cv::Sobel(img_gray, img_sobel, CV_8U, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);
    cv::threshold(img_sobel, img_threshold, 0, 255, CV_THRESH_OTSU+CV_THRESH_BINARY);
    element = getStructuringElement(cv::MORPH_RECT, cv::Size(17, 3) );
    cv::morphologyEx(img_threshold, img_threshold, CV_MOP_CLOSE, element); //Does the trick
    std::vector< std::vector< cv::Point> > contours;
    cv::findContours(img_threshold, contours, 0, 1); 
    std::vector<std::vector<cv::Point> > contours_poly( contours.size() );
    for( int i = 0; i < contours.size(); i++ )
        if (contours[i].size()>100)
        { 
            cv::approxPolyDP( cv::Mat(contours[i]), contours_poly[i], 3, true );
            cv::Rect appRect( boundingRect( cv::Mat(contours_poly[i]) ));
            if (appRect.width>appRect.height) 
                boundRect.push_back(appRect);
        }
    return boundRect;
}

用法：

int main(int argc,char** argv)
{
    //Read
    cv::Mat img1=cv::imread("side_1.jpg");
    cv::Mat img2=cv::imread("side_2.jpg");
    //Detect
    std::vector<cv::Rect> letterBBoxes1=detectLetters(img1);
    std::vector<cv::Rect> letterBBoxes2=detectLetters(img2);
    //Display
    for(int i=0; i< letterBBoxes1.size(); i++)
        cv::rectangle(img1,letterBBoxes1[i],cv::Scalar(0,255,0),3,8,0);
    cv::imwrite( "imgOut1.jpg", img1);  
    for(int i=0; i< letterBBoxes2.size(); i++)
        cv::rectangle(img2,letterBBoxes2[i],cv::Scalar(0,255,0),3,8,0);
    cv::imwrite( "imgOut2.jpg", img2);  
    return 0;
}

结果：

a。 element = getStructuringElement（cv :: MORPH_RECT，cv :: Size（17，3））;

a. element = getStructuringElement(cv::MORPH_RECT, cv::Size(17, 3) );

b。 element = getStructuringElement（cv :: MORPH_RECT，cv :: Size（30，30））;

b. element = getStructuringElement(cv::MORPH_RECT, cv::Size(30, 30) );

结果与上述其他图片类似。

Results are similar for the other image mentioned.

这篇关于提取文本OpenCV的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！