Detect rectangle in image and crop

Question

I have scanned images of many handwritten digits inside a rectangle

But, I don't know how to crop the images containing digits and save them just by giving the same name to each row

import cv2

img = cv2.imread('Data\Scan_20170612_4.jpg')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

gray = cv2.bilateralFilter(gray, 11, 17, 17)

edged = cv2.Canny(gray, 30, 200)

_, contours, hierarchy = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

i = 0

for c in contours:

    peri = cv2.arcLength(c, True)

    approx = cv2.approxPolyDP(c, 0.09 * peri, True)

    if len(approx) == 4:

        screenCnt = approx

        cv2.drawContours(img, [screenCnt], -1, (0, 255, 0), 3)

        cv2.imwrite('cropped\\' + str(i) + '_img.jpg', img)

        i += 1

Answer 1

Have a look at my code:

import cv2

import numpy as np

fileName = ['9','8','7','6','5','4','3','2','1','0']

img = cv2.imread('Data\Scan_20170612_17.jpg')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

gray = cv2.bilateralFilter(gray, 11, 17, 17)

kernel = np.ones((5,5),np.uint8)

erosion = cv2.erode(gray,kernel,iterations = 2)

kernel = np.ones((4,4),np.uint8)

dilation = cv2.dilate(erosion,kernel,iterations = 2)

edged = cv2.Canny(dilation, 30, 200)

_, contours, hierarchy = cv2.findContours(edged, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

rects = [cv2.boundingRect(cnt) for cnt in contours]

rects = sorted(rects,key=lambda  x:x[1],reverse=True)

i = -1

j = 1

y_old = 5000

x_old = 5000

for rect in rects:

    x,y,w,h = rect

    area = w * h

    if area > 47000 and area < 70000:

        if (y_old - y) > 200:

            i += 1

            y_old = y

        if abs(x_old - x) > 300:

            x_old = x

            x,y,w,h = rect

            out = img[y+10:y+h-10,x+10:x+w-10]

            cv2.imwrite('cropped\\' + fileName[i] + '_' + str(j) + '.jpg', out)

            j+=1

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