CODEWITHSUNDEEP
pythonopencvimage-processingocrimage-segmentation
L14n
L14n

Reputation: 51

Clean image for OCR

I've been trying to clean this image for OCR but getting mixed results:

enter image description here

Best I achieved:

enter image description here

def image_smoothening(img):
    ret1, th1 = cv2.threshold(img, 180, 255, cv2.THRESH_BINARY)
    ret2, th2 = cv2.threshold(th1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    blur = cv2.GaussianBlur(th2, (1, 1), 0)
    ret3, th3 = cv2.threshold(
        blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return th3
    
def remove_noise_and_smooth(img):
    filtered = cv2.adaptiveThreshold(img.astype(
        np.uint8), 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 45, 3)
    kernel = np.ones((1, 1), np.uint8)
    opening = cv2.morphologyEx(filtered, cv2.MORPH_OPEN, kernel)
    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
    img = image_smoothening(img)
    or_image = cv2.bitwise_or(img, closing)
    return or_image

Any clue as to what I'm missing?

Upvotes: 1

Views: 462

Answers (2)

Karson
Karson

Reputation: 120

My MATLAB code to solve it. I know you are writing in Python so you'll have to translate.

%Read in
im = imread('DuQy7.png');
%Convert to grayscale
img = rgb2gray(im);
img = rescale(img);
%Binarize with threshold of 0.7/1.0
imbw = imbinarize(img,0.7/1);
%Flip blacks/whites
imbw = imcomplement(imbw);
%Label, L is labelled image, n is # of labels
[L,n] = bwlabeln(imbw);

count = zeros(n,1);
[y,x] = size(L);

%Get count for each label
L = uint8(L);
for j=1:y
    for i=1:x
        if L(j,i) ~= 0
            count(L(j,i)) = count(L(j,i)) + 1;
        end
    end
end

%Find label with most values in image
max = 0;
maxi = 1;
for index=1:n
    if max < count(index)
        max = count(index);
        maxi = index;
    end
end

%Replace large region and color other labels to white
for j=1:y
    for i=1:x
        if L(j,i) == maxi
            L(j,i) = 0;
        elseif L(j,i) ~= 0
            L(j,i) = 256;
        end
    end
end

%view and save
imshow(L)
imwrite(L,'outputTXT.bmp');

output-TXT.png

You could probably better adjust the threshold to better cut out background regions that got included. You could also look for labelled regions that are very small and remove them since they are probably erroneously included.

Some parts of the background are going to be impossible to get rid of since they are indistinguishable from the actual symbols. For example, between symbol x2,y1 and x2,y2 there is a black background region between the outlined white which is the same value as the symbols. Therefore it would be very difficult to parse out.

Upvotes: 4

fmw42
fmw42

Reputation: 53174

You can do "division normalization" in Python/OpenCV to remove the background. But that will not help with the outline font issue.

Input:

enter image description here

import cv2
import numpy as np

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

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

# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_RECT , (3,3))
smooth = cv2.morphologyEx(gray, cv2.MORPH_DILATE, kernel)

# alternate blur in place of morphology
#smooth = cv2.GaussianBlur(gray, (15,15), 0)

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

# threshold
result = cv2.threshold(division, 0, 255, cv2.THRESH_OTSU )[1] 

# save results
cv2.imwrite('img_thresh.png',result)

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

Result:

enter image description here

Upvotes: 0

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