Detect space between text (OpenCV, Python)

Question

I have the following code (which is in fact just 1 part of 4 needed to run all the project I am working on..):

#python classify.py --model models/svm.cpickle         


        

Answer 1

Used this code to do the job. It detects region of text/digits in images.

import cv2

image = cv2.imread("C:\\Users\\Bob\\Desktop\\PyHw\\images\\test5.png")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # grayscale
_,thresh = cv2.threshold(gray,150,255,cv2.THRESH_BINARY_INV) # threshold
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
dilated = cv2.dilate(thresh,kernel,iterations = 13) # dilate
_, contours, hierarchy = cv2.findContours(dilated,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) # get contours


idx =0
# for each contour found, draw a rectangle around it on original image
for contour in contours:

    idx += 1

    # get rectangle bounding contour
    [x,y,w,h] = cv2.boundingRect(contour)

    # discard areas that are too large
    if h>300 and w>300:
        continue

    # discard areas that are too small
    if h<40 or w<40:
        continue

    # draw rectangle around contour on original image
    #cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,255),2)

    roi = image[y:y + h, x:x + w]

    cv2.imwrite('C:\\Users\\Bob\\Desktop\\' + str(idx) + '.jpg', roi)

    cv2.imshow('img',roi)
    cv2.waitKey(0)

The code is based on this other question/answer: Extracting text OpenCV

Answer 2

This is a starter solution.

I don't have anything in Python for the time being but it shouldn't be hard to convert this plus the OpenCV function calls are similar and I've linked them below.

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TLDR;

Find the centre of your boundingRects, then find the distance between them. If one rect is a certain threshold away, you may assume it as being a space.

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First, find the centres of your bounding rectangles

vector<Point2f> centres;

for(size_t index = 0; index < contours.size(); ++index)
{
    Moments moment = moments(contours[index]);

    centres.push_back(Point2f(static_cast<float>(moment.m10/moment.m00), static_cast<float>(moment.m01/moment.m00)));
}

(Optional but recommended)

You can draw the centres to have a visual understanding of them.

for(size_t index = 0; index < centres.size(); ++index)
{
    Scalar colour = Scalar(255, 255, 0);
    circle(frame, circles[index], 2, colour, 2);
}

With this, just iterate through them confirming that the distance to the next one is within a reasonable threshold

for(size_t index = 0; index < centres.size(); ++index)
{
    // this is just a sample value. Tweak it around to see which value actually makes sense
    double distance = 0.5;
    Point2f current = centres[index];
    Point2f nextPoint = centres[index + 1];

    // norm calculates the euclidean distance between two points
    if(norm(nextPoint - current) >= distance)
    {
        // TODO: This is a potential space??
    }
}

You can read more about moments, norm and circle drawing calls in Python.

Happy coding, Cheers mate :)