Python OpenCV skew correction for OCR

Question

Currently, I am working on an OCR project where I need to read the text off of a label (see example images below). I am running into issues with the image skew and I need he

Answer 1

Here's an implementation of the Projection Profile Method to determine skew. After obtaining a binary image, the idea is rotate the image at various angles and generate a histogram of pixels in each iteration. To determine the skew angle, we compare the maximum difference between peaks and using this skew angle, rotate the image to correct the skew

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Left (original), Right (corrected)

import cv2
import numpy as np
from scipy.ndimage import interpolation as inter

def correct_skew(image, delta=1, limit=5):
    def determine_score(arr, angle):
        data = inter.rotate(arr, angle, reshape=False, order=0)
        histogram = np.sum(data, axis=1)
        score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
        return histogram, score

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] 

    scores = []
    angles = np.arange(-limit, limit + delta, delta)
    for angle in angles:
        histogram, score = determine_score(thresh, angle)
        scores.append(score)

    best_angle = angles[scores.index(max(scores))]

    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, \
              borderMode=cv2.BORDER_REPLICATE)

    return best_angle, rotated

if __name__ == '__main__':
    image = cv2.imread('1.png')
    angle, rotated = correct_skew(image)
    print(angle)
    cv2.imshow('rotated', rotated)
    cv2.imwrite('rotated.png', rotated)
    cv2.waitKey()