Automatic Skew correction using opencv

Question

I want a way to automatically detect and correct skew of a image of a receipt, I tried to find variance between the rows for various angles of rotation and choose the angle which has the the maximum variance. To calculate variance I did the following:

1.For each row I calculated the sum of the pixels values and stored it in a list.

2.Found the the variance of the list using np.var(list)

    src = cv.imread(f_name, cv.IMREAD_GRAYSCALE)
    blurred=median = cv.medianBlur(src,9)
    ret,thresh2 = cv.threshold(src,127,255,cv.THRESH_BINARY_INV)
    height, width = thresh2.shape[:2]
    print(height,width)
    res=[-1,0]
    for angle in range(0,100,10):

        rotated_temp=deskew(thresh2,angle)
        cv.imshow('rotated_temp',rotated_temp)
        cv.waitKey(0)
        height,width=rotated_temp.shape[:2]
        li=[]
        for i in range(height):
            sum=0
            for j in range(width):
                sum+=rotated_temp[i][j]
            li.append(sum)
        curr_variance=np.var(li)
        print(curr_variance,angle)
        if(curr_variance>res[0]):
            res[0]=curr_variance
            res[1]=angle


    print(res)
    final_rot=deskew(src,res[1])
    cv.imshow('final_rot',final_rot)
    cv.waitKey(0)

However the variance for a skewed image is coming to be more than the properly aligned image,is there any way to correct this

variance for the horizontal text aligned image(required):122449908.009789
variance for the vertical text aligned image :1840071444.404522

I have tried using HoughLines However since the spacing between the text is too less vertical lines are detected,hence this also fails

Any modifications or other approaches are appreciated

Try dilating the image and then eroding it. That should generate big blobs that you can detect for largest length — Ander Biguri, Jan 25 '19 at 12:08

score 0 · Answer 1 · edited Jun 20 '20 at 09:12

Working code for skew correction

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image as im
from scipy.ndimage import interpolation as inter

input_file = r'E:\flaskV8\test1.jpg'

img = im.open(input_file)

convert to binary

wd, ht = img.size
pix = np.array(img.convert('1').getdata(), np.uint8)
bin_img = 1 - (pix.reshape((ht, wd)) / 255.0)
plt.imshow(bin_img, cmap='gray')
plt.savefig(r'E:\flaskV8\binary.png')
def find_score(arr, angle):
    data = inter.rotate(arr, angle, reshape=False, order=0)
    hist = np.sum(data, axis=1)
    score = np.sum((hist[1:] - hist[:-1]) ** 2)
    return hist, score
delta = 1
limit = 5
angles = np.arange(-limit, limit+delta, delta)
scores = []
for angle in angles:
    hist, score = find_score(bin_img, angle)
    scores.append(score)
best_score = max(scores)
    best_angle = angles[scores.index(best_score)]
    print('Best angle: {}'.format(best_angle))
    data = inter.rotate(bin_img, best_angle, reshape=False, order=0)
    img = im.fromarray((255 * data).astype("uint8")).convert("RGB")
    img.save(r'E:\flaskV8\skew_corrected.png')

Automatic Skew correction using opencv

1 Answers1

Working code for skew correction

convert to binary