CODEWITHSUNDEEP
pythonopencvocrimage-thresholding
singrium
singrium

Reputation: 3026

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

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)

enter image description here 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:

enter image description here

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?

Upvotes: 1

Views: 893

Answers (2)

singrium
singrium

Reputation: 3026

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:

enter image description here

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.

Upvotes: 3

Ishara Dayarathna
Ishara Dayarathna

Reputation: 3601

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 )

Upvotes: 1

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