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
You can detect text by finding close edge elements (inspired from a LPD):
#include "opencv2/opencv.hpp"
std::vector detectLetters(cv::Mat img)
{
std::vector 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 > 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;
}
Usage:
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 letterBBoxes1=detectLetters(img1);
std::vector 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;
}
Results:
a. element = getStructuringElement(cv::MORPH_RECT, cv::Size(17, 3) );
b. element = getStructuringElement(cv::MORPH_RECT, cv::Size(30, 30) );
Results are similar for the other image mentioned.