Simple version of deskewing

deskew.py

import sys

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

input_file = sys.argv[1]

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('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: {}'.formate(best_angle))

# correct skew
data = inter.rotate(bin_img, best_angle, reshape=False, order=0)
img = im.fromarray((255 * data).astype("uint8")).convert("RGB")
img.save('skew_corrected.png')