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I would use binary threshold, morphology and contours to solve this problem.

Step 1 : get the copy with signature

cv::Mat const map_01 = cv::imread("map01.jpg");

Step 2 : blur the image and convert it to binary image

cv::Mat binarize;
cv::cvtColor(map_01, binarize, CV_BGR2GRAY);
cv::GaussianBlur(map_01, map_01, {5,5}, 3,3);
cv::threshold(binarize, binarize, 0, 255,
cv::THRESH_BINARY_INV | cv::THRESH_OTSU);

Step 3 : Use morphology to remove small blobs and make text blobs more obvious

auto mrect = cv::getStructuringElement(cv::MORPH_RECT, {3,3});
cv::erode(binarize, binarize, mrect);
mrect = cv::getStructuringElement(cv::MORPH_RECT, {7,5});
cv::morphologyEx(binarize, binarize, cv::MORPH_DILATE, mrect, {-1,-1}, 7);

Step 4 : find contours and remove those with invalid aspect ratio

std::vector<std::vector<cv::Point>> contours;
cv::findContours(binarize.clone(), contours, CV_RETR_EXTERNAL,
                 CV_CHAIN_APPROX_SIMPLE);
auto rit = std::remove_if(std::begin(contours), std::end(contours),
               [](auto const &ct)
{
    auto const rect = cv::boundingRect(ct);
    return rect.height > rect.width;
});
contours.erase(rit, std::end(contours));

Step 5 : Find the data of each rectangle

for(auto const &ct : contours){
    //get the information of each rect
    cv::Mat temp = map_01.clone();
    std::cout<<cv::boundingRect(ct)<<std::endl;
    cv::rectangle(temp, cv::boundingRect(ct), {255,0,0});
    cv::imshow("temp", temp);
    cv::waitKey();
    cv::destroyAllWindows();       
}

The bounding rects found by this solution are

Step 6 : Extract text location according to the location and size of the bounding rects, this step is quite tedious.

Not a very good one, I believe there exist better solution, like find the difference between two images.

Source codes locate at github.

I would use binary threshold, morphology and contours to solve this problem.

Step 1 : get the copy with signature

cv::Mat const map_01 = cv::imread("map01.jpg");

Step 2 : blur the image and convert it to binary image

cv::Mat binarize;
cv::cvtColor(map_01, binarize, CV_BGR2GRAY);
cv::GaussianBlur(map_01, map_01, {5,5}, 3,3);
cv::threshold(binarize, binarize, 0, 255,
cv::THRESH_BINARY_INV | cv::THRESH_OTSU);

Step 3 : Use morphology to remove small blobs and make text blobs more obvious

auto mrect = cv::getStructuringElement(cv::MORPH_RECT, {3,3});
cv::erode(binarize, binarize, mrect);
mrect = cv::getStructuringElement(cv::MORPH_RECT, {7,5});
cv::morphologyEx(binarize, binarize, cv::MORPH_DILATE, mrect, {-1,-1}, 7);

Step 4 : find contours and remove those with invalid aspect ratio

std::vector<std::vector<cv::Point>> contours;
cv::findContours(binarize.clone(), contours, CV_RETR_EXTERNAL,
                 CV_CHAIN_APPROX_SIMPLE);
auto rit = std::remove_if(std::begin(contours), std::end(contours),
               [](auto const &ct)
{
    auto const rect = cv::boundingRect(ct);
    return rect.height > rect.width;
});
contours.erase(rit, std::end(contours));

Step 5 : Find the data of each rectangle

for(auto const &ct : contours){
    //get the information of each rect
    cv::Mat temp = map_01.clone();
    std::cout<<cv::boundingRect(ct)<<std::endl;
    cv::rectangle(temp, cv::boundingRect(ct), {255,0,0});
    cv::imshow("temp", temp);
    cv::waitKey();
    cv::destroyAllWindows();       
}

The bounding rects found by this solution are

Step 6 : Extract text location according to the location and size of the bounding rects, this step is quite tedious.

Not a very good one, I believe there exist better solution, like find the difference between two images.

Source codes locate at github.