deskew's Introduction

Deskew

//Note: Skew is measured in degrees. Deskewing is a process whereby skew is removed by rotating an image by the same amount as its skew but in the opposite direction. This results in a horizontally and vertically aligned image where the text runs across the page rather than at an angle.

Skew detection and correction in images containing text

Image with skew

Image after deskew

Cli usage

Get the skew angle:

deskew input.png

Deskew an image:

deskew --output output.png input.png

Lib usage

scikit-image:

import numpy as np
from skimage import io
from skimage.color import rgb2gray
from skimage.transform import rotate

from deskew import determine_skew

image = io.imread('input.png')
grayscale = rgb2gray(image)
angle = determine_skew(grayscale)
rotated = rotate(image, angle, resize=True) * 255
io.imsave('output.png', rotated.astype(np.uint8))

OpenCV:

import math
from typing import Tuple, Union

import cv2
import numpy as np

from deskew import determine_skew


def rotate(
        image: np.ndarray, angle: float, background: Union[int, Tuple[int, int, int]]
) -> np.ndarray:
    old_width, old_height = image.shape[:2]
    angle_radian = math.radians(angle)
    width = abs(np.sin(angle_radian) * old_height) + abs(np.cos(angle_radian) * old_width)
    height = abs(np.sin(angle_radian) * old_width) + abs(np.cos(angle_radian) * old_height)

    image_center = tuple(np.array(image.shape[1::-1]) / 2)
    rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
    rot_mat[1, 2] += (width - old_width) / 2
    rot_mat[0, 2] += (height - old_height) / 2
    return cv2.warpAffine(image, rot_mat, (int(round(height)), int(round(width))), borderValue=background)

image = cv2.imread('input.png')
grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
angle = determine_skew(grayscale)
rotated = rotate(image, angle, (0, 0, 0))
cv2.imwrite('output.png', rotated)