# Detect & Correct Skew In Images Using Python

When scanning a document, a slight skew gets into the scanned image. If you are using the scanned image to extract information from it, detecting and correcting skew is crucial.

There are several techniques that are used to skew correction.

• Projection profile method
• Hough transform

• Topline method

• Scanline method

However, projection profile method is the simplest and easiest way to determine skew in documents in the range ±5°. Lets take a part of scanned image and see how to correct skew.

In this method, we will convert image to black (absence of pixel) & white (presence of pixel). Now image is projected vertically to get a histogram of pixels. Now image is rotated at various angles and above process is repeated. Wherver we find maximum diffrence between peaks, that will be the best angle.

``````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

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')
``````

Results: Original Image Black & white image Histogram of image Scores at various angles Histogram at 2° (best angle) Skew corrected image

Here we have done only one iteration to find best angle. To get better accuracy, we can search over at (2 ± 0.5)°. This process can be repeated until we find a suitable level of accuracy.