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to solve this kind of problems i want to share an approach based on cv::erode

with the code below i tried to find text blocks. the result is OK by manuel parameters of cv::erode

cv::Mat kernel = cv::Mat::ones(10, 5, CV_8U);
erode(src_gray,src_gray, kernel, Point(-1,-1),2);

with a bit effort it is possible to develop an algorithm for manually entered values.

image after binarization and cv::erode

final result

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

using namespace cv;
using namespace std;

int main( int, char** argv )
{
    Mat src,src_gray;
    src = imread("27411.jpg");
    if (src.empty())
    {
        cerr << "No image supplied ..." << endl;
        return -1;
    }
    cvtColor( src, src_gray, COLOR_BGR2GRAY );
    src_gray = src_gray >127;

    vector<vector<Point> > contours;
    vector<Vec4i> hierarchy;

    cv::Mat kernel = cv::Mat::ones(10, 5, CV_8U);
    erode(src_gray,src_gray, kernel, Point(-1,-1),2);
    imshow( "src_gray", src_gray );

    findContours( src_gray, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE, Point(0, 0) );
    for( size_t i = 0; i< contours.size(); i++ )
    {
        Scalar color = Scalar( 0,255,0 );
        Rect R = boundingRect(Mat(contours[i]));
        rectangle(src,R,color);

    }
    imshow( "result", src );

    waitKey(0);
    return(0);
}