Is there a better way to separate the writing from the background?

Question

I am working on a project where I should apply and OCR on some documents.
The first step is to threshold the image and let only the writing (whiten the background).

Example of an input image: (For the GDPR and privacy reasons, this image is from the Internet)

Here is my code:

import cv2
import numpy as np


image = cv2.imread('b.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
h = image.shape[0]
w = image.shape[1]
for y in range(0, h):
    for x in range(0, w):
        if image[y, x] >= 120:
            image[y, x] = 255
        else:
            image[y, x] = 0
cv2.imwrite('output.jpg', image)

Here is the result that I got:

When I applied pytesseract to the output image, the results were not satisfying (I know that an OCR is not perfect). Although I tried to adjust the threshold value (in this code it is equal to 120), the result was not as clear as I wanted.

Is there a way to make a better threshold in order to only keep the writing in black and whiten the rest?

Its not a good idea to post your personal/someone ID online. — Sufiyan Ghori, Oct 31 '18 at 21:36
Thank you, I am totally aware of that, it is an image I downloaded from the Internet — singrium, Oct 31 '18 at 21:37
The bottom part looks pretty clean, is that what you're trying to decode? — Mark Ransom, Nov 01 '18 at 02:51
@MarkRansom, not only the bottom. Mainly the name, family name, date and place of birth — singrium, Nov 01 '18 at 08:13

score 3 · Answer 1 · answered Nov 05 '18 at 20:05

After digging deep in StackOverflow questions, I found this answer which is about removing watermark using opencv. I adapted the code to my needs and this is what I got:

import numpy as np
import cv2


image = cv2.imread('a.png')
img = image.copy()

alpha =2.75
beta = -160.0

denoised = alpha * img + beta
denoised = np.clip(denoised, 0, 255).astype(np.uint8)

#denoised = cv2.fastNlMeansDenoising(denoised, None, 31, 7, 21)

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

h = img.shape[0]
w = img.shape[1]

for y in range(0, h):
    for x in range(0, w):
        if img[y, x] >= 220:
            img[y, x] = 255
        else:
            img[y, x] = 0

cv2.imwrite('outpu.jpg', img)

Here is the output image:

The good thing about this code is that it gives good results not only with this image, but also with all the images that I tested.

I hope it helps anyone who had the same problem.

score 1 · Accepted Answer · edited Nov 01 '18 at 08:43

1

You can use adaptive thresholding. From documentation :

In this, the algorithm calculate the threshold for a small regions of the image. So we get different thresholds for different regions of the same image and it gives us better results for images with varying illumination.

import numpy as np
import cv2



image = cv2.imread('b.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.medianBlur(image ,5)

th1 = cv2.adaptiveThreshold(image,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
            cv2.THRESH_BINARY,11,2)
th2 = cv2.adaptiveThreshold(image,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv2.THRESH_BINARY,11,2)
cv2.imwrite('output1.jpg', th1 )
cv2.imwrite('output2.jpg', th2 )

edited Nov 01 '18 at 08:43

singrium

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answered Nov 01 '18 at 03:23

Ishara Madhawa

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Unfortunately, this didn't give a better result too: the background is still noisy so that the writing is not clear yet. – singrium Nov 01 '18 at 08:20
you need to change the last two parameters of `adaptiveThreshold` method accordingly in order to get better results – Ishara Madhawa Nov 01 '18 at 08:34
In fact, I tried to adjust the parameters, but it didn't give a good result. – singrium Nov 02 '18 at 08:01

Is there a better way to separate the writing from the background?

2 Answers2