I am planning to split the questions from

and split into individual components like this?

CodePudding user response：

This is a classic situation for

Here's where the interesting section happens. We assume that adjacent text/characters are part of the same question so we merge individual words into a single contour. A question is a section of words that are close together so we dilate to connect them all together.

Individual questions highlighted in green

Extracted ROI questions

Code

import cv2
import numpy as np

# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7,7), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV   cv2.THRESH_OTSU)[1]

# Create rectangular structuring element and dilate
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
dilate = cv2.dilate(thresh, kernel, iterations=4)

# Find contours and extract each question
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
question_number = 0
for c in cnts:
    # Get bounding box of each question, crop ROI, and save
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x   w, y   h), (36,255,12), 2)
    question = original[y:y h, x:x w]
    cv2.imwrite('question_{}.png'.format(question_number), question)
    question_number  = 1

cv2.imshow('thresh', thresh)
cv2.imshow('dilate', dilate)
cv2.imshow('image', image)
cv2.waitKey()

CodePudding user response：

We may solve it using (mostly) morphological operations:

• Read the input image as grayscale.
• Apply thresholding with inversion.
  Automatic thresholding using cv2.THRESH_OTSU is working well.
• Apply opening morphological operation for removing small artifacts (using the kernel np.ones(1, 3))
• Dilate horizontally with very long horizontal kernel - make horizontal lines out of the text lines.
• Apply closing vertically - create two large clusters.
  The size of the vertical kernel should be tuned according to the typical gap.
• Finding connected components with statistics.
• Iterate the connected components and crop the relevant area in the vertical direction.

Complete code sample:

import cv2
import numpy as np

img = cv2.imread('scanned_image.png', cv2.IMREAD_GRAYSCALE)  # Read image as grayscale

thesh = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU   cv2.THRESH_BINARY_INV)[1]  # Apply automatic thresholding with inversion.

thesh = cv2.morphologyEx(thesh, cv2.MORPH_OPEN, np.ones((1, 3), np.uint8))  # Apply opening morphological operation for removing small artifacts.

thesh = cv2.dilate(thesh, np.ones((1, img.shape[1]), np.uint8))  # Dilate horizontally - make horizontally  lines out of the text.

thesh = cv2.morphologyEx(thesh, cv2.MORPH_CLOSE, np.ones((50, 1), np.uint8))  # Apply closing vertically - create two large clusters

nlabel, labels, stats, centroids = cv2.connectedComponentsWithStats(thesh, 4)  # Finding connected components with statistics

parts_list = []

# Iterate connected components:
for i in range(1, nlabel):
    top = int(stats[i, cv2.CC_STAT_TOP])  # Get most top y coordinate of the connected component
    height = int(stats[i, cv2.CC_STAT_HEIGHT])  # Get the height of the connected component

    roi = img[top-5:top height 5, :]  # Crop the relevant part of the image (add 5 extra rows from top and bottom).
    parts_list.append(roi.copy()) # Add the cropped area to a list

    cv2.imwrite(f'part{i}.png', roi)  # Save the image part for testing
    cv2.imshow(f'part{i}', roi)  # Show part for testing

# Show image and thesh testing
cv2.imshow('img', img)
cv2.imshow('thesh', thesh)

cv2.waitKey()
cv2.destroyAllWindows()

Results:

Stage 1:

Stage 2:

Stage 3:

Stage 4:

Top area:

Bottom area: