How Do You Remove The Unnecessary Blackness From Binary Images (mainly Caused By Dark Areas/noise)

I am working on Images of Textbook pages such as questions and handwritten notes and want the binary image for that for few different tasks mainly the OCR. But the problem is that

Solution 1:

Here is one way to do that in Python/OpenCV using division normalization.

• Read the input
• Convert to gray
• Smooth with Gaussian blur
• Divide gray image by smoothed image
• Apply unsharp masking to sharpen
• Apply Otsu threshold
• Save results

Input:

import cv2
import numpy as np
import skimage.filters as filters

# read the image
img = cv2.imread('math.png')

# convert to gray
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# blur
smooth = cv2.GaussianBlur(gray, (95,95), 0)

# divide gray by morphology image
division = cv2.divide(gray, smooth, scale=255)

# sharpen using unsharp masking
sharp = filters.unsharp_mask(division, radius=1.5, amount=1.5, multichannel=False, preserve_range=False)
sharp = (255*sharp).clip(0,255).astype(np.uint8)

# threshold
thresh = cv2.threshold(sharp, 0, 255, cv2.THRESH_OTSU )[1] 

# save results
cv2.imwrite('math_division.jpg',division)
cv2.imwrite('math_division_sharp.jpg',sharp)
cv2.imwrite('math_division_thresh.jpg',division)

# show results
cv2.imshow('smooth', smooth)  
cv2.imshow('division', division)  
cv2.imshow('sharp', sharp)  
cv2.imshow('thresh', thresh)  
cv2.waitKey(0)
cv2.destroyAllWindows()

Division image:

Sharpened image:

Thresholded image: