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Deskew Scanned Document

Deskewing document is one of the essential preprcessing step before OCR and HTR. This repo contain proposed method for document deskewing mainly via Fourier Transform.

Techniques

  • Fourier Transform

Fast Fourier Transform

  • Non Local Means Denoising

Article about NLMD

Implementation from scratch

Requirements

  • OpenCV
  • Scikit-image
  • Numpy

Algorithm steps

  • Change image to gray format
  • Morphological operations
  • Fast Fourier Transform
  • FNlM Denoising and thresholding of FFT output
  • Determines the peaks
  • Calculate angle using slope of drawing line

P.S.

This method works well if angle of skew is between -60 and 60 degrees*

Vizualisation of each step

Code usage

from deskew_fft import RotateDoc
from skimage.transform import rotate
import skimage.io as io
import matplotlib.pyplot as plt

path = 'test_image_1.jpg'
rotate_doc = RotateDoc(
            img_path=path,
            visualize=1,
            resize_ratio=2,
            peak_top_bottom=True,
            synth=False
            )

image = io.imread(path)
ag = rotate_doc.deskewImage()
rotated_image = rotate(image,ag)
if rotate_doc.visualize:
    plt.imshow(rotated_image)
    plt.show()

Synthetic test

In this test document manually rotated from -60 to 60 degree and check performance of methdod.

Rotate -60 degree Deskew 63 degree
Rotate -50 degree Deskew 53 degree
Rotate -40 degree Deskew 46 degree
Rotate -30 degree Deskew 35 degree
Rotate -20 degree Deskew 25 degree
Rotate -10 degree Deskew 12 degree
Rotate 10 degree Deskew -12 degree
Rotate 20 degree Deskew -24 degree
Rotate 30 degree Deskew -35 degree
Rotate 40 degree Deskew -47 degree
Rotate 50 degree Deskew -55 degree
Rotate 60 degree Deskew -65 degree

Synthetic test code

from deskew_fft import RotateDoc
from skimage.transform import rotate
import skimage.io as io
import matplotlib.pyplot as plt

path = 'test_image_1.jpg'
for angle in range(-60,70,10):
    rotated_image = rotate(io.imread(path),angle)
    rotate_doc = RotateDoc(
                            img_path=path,
                            visualize=1,
                            resize_ratio=2,
                            peak_top_bottom=True,
                            synth=True
                            )
    ag = rotate_doc.deskewImage(angle=angle)
    rotated_image = rotate(rotated_image,ag)
    if rotate_doc.visualize:
        plt.imshow(rotated_image)
        plt.show()
    print('-'*100)