How to find the contour of a completed form scanned image?

Question

I would like to detect the contour of the completed form in this scan.

Ideally I would want to find the corners of the table painted with red.

My final goal is to detect that the whole document was scanned and that the four corners are within the boundaries of the scan.

I used OpenCV from python - but it was not able to find the contour of the big container.

Any ideas?

Answer 1

With the observation that the form can be identified using the table grid, here's a simple approach:

1. Obtain binary image. Load the image, grayscale, Gaussian blur, then Otsu's threshold to get a binary image

2. Find horizontal sections. We create a horizontal shaped kernel and find horizontal table lines and draw onto a mask

3. Find vertical sections. We create a vertical shaped kernel and find vertical table lines and draw onto a mask

4. Fill text document body and morph open. We perform morph operations to close the table then find contours and fill the mask to obtain a contour of the shape. This step fulfills your needs since you can just find contours on the mask but we can go further and extract only the desired sections.

5. Perform four-point perspective transform. We find contours, sort for the largest contour, sort using contour approximation then perform a four-point perspective transform to obtain a birds eye view of the image.

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Here's the results:

Input image

Detected contour to extract highlighted in green

Output after 4-point perspective transform

Code

import cv2
import numpy as np
from imutils.perspective import four_point_transform

# Load image, create mask, grayscale, and Otsu's threshold
image = cv2.imread('1.jpg')
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,3)

# Find horizontal sections and draw on mask 
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (80,1))
detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(mask, [c], -1, (255,255,255), -1)

# Find vertical sections and draw on mask 
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(mask, [c], -1, (255,255,255), -1)

# Fill text document body
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
close = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel, iterations=3)
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(mask, [c], -1, 255, -1)

# Perform morph operations to remove noise
# Find contours and sort for largest contour
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, close_kernel, iterations=5)
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None

for c in cnts:
    # Perform contour approximation
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)
    if len(approx) == 4:
        displayCnt = approx
        break

# Obtain birds' eye view of image
warped = four_point_transform(image, displayCnt.reshape(4, 2))

cv2.imwrite('mask.png', mask)
cv2.imwrite('thresh.png', thresh)
cv2.imwrite('warped.png', warped)
cv2.imwrite('opening.png', opening)