Processing image for reducing noise with OpenCV in Python

Question

I want to apply some kind of preprocessing to this image so that text can be more readable, so that later I can read text from image. I'm new to this so I do not know what should I do, should I increase contrast or should I reduce noise, or something else. Basically, I want to remove these gray areas on the image and keep only black letters (as clear as they can be) and white background.

import cv2

img = cv2.imread('slika1.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', img)
cv2.waitKey(0)  


thresh = 200
img = cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)[1]
cv2.imshow('filter',img)
cv2.waitKey(0)

I read the image and applied threshold to the image but I needed to try 20 different thresholds until I found one that gives results. Is there any better way to solve problems like this? The problem is that I can get different pictures with different size of gray areas, so sometime I do not need to apply any kind of threshold, and sometimes I do, because of that I think that my solution with threshold is not that good.

For this image, my code works good:

But for this it gives terrible results:

fmw42 · Accepted Answer · 2020-09-24 00:28:05Z

Try division normalization in Python/OpenCV. Divide the input by its blurred copy. Then sharpen. You may want to crop the receipt better or mask out the background first.

Input:

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

# read the image
img = cv2.imread('receipt2.jpg')

# 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)


# save results
cv2.imwrite('receipt2_division.png',division)
cv2.imwrite('receipt2_division_sharp.png',sharp)


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

Division result:

Sharpened result:

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