Detecting rotation and line spacing of image of page of text using Radon transform

rotation_spacing.py

# -*- coding: utf-8 -*-
"""
Inspired in https://gist.github.com/jsfenfen/4c615775007b802117b7

Automatically detect rotation and line spacing of an image of text using
Fourier transforms

If image is rotated by the inverse of the output, the lines will be
horizontal (though they may be upside-down depending on the original image)

"""

import numpy as np
import cv2 as cv
from scipy.ndimage import filters
import matplotlib.pyplot as plt
from matplotlib.mlab import rms_flat


filename = 'skew-linedetection.png'

# Load file, converting to grayscale
img = cv.imread(filename, 0)
plt.subplot(2, 2, 1)
plt.imshow(img)

# Do the 2D Fourier transform and display the result
# First apply 2D windowing
height, width = image.shape
window_matrix = np.outer(np.blackman(height), np.blackman(width))
windowed = image * window_matrix
# FFT 2D
fft = np.fft.fft2(image)
fft = np.fft.fftshift(f)
fft = np.log(np.abs(fshift))
# Reescale into a square shape to interpret angles properly
side = max(height, width)
fft = cv.resize(magnitude_spectrum, (side, side))

plt.subplot(2, 2, 2)
plt.imshow(fft)

# TO BE CONTINUED....

# Find the RMS value of each row and find "busiest" rotation,
# where the transform is lined up perfectly with the alternating dark
# text and white lines
r = array([rms_flat(line) for line in sinogram.transpose()])
rotation = argmax(r)
print('Rotation: {:.2f} degrees'.format(90 - rotation))
plt.axhline(rotation, color='r')

# Plot the busy row
row = sinogram[:, rotation]
N = len(row)
plt.subplot(2, 2, 3)
plt.plot(row)

# Take spectrum of busy row and find line spacing
window = blackman(N)
spectrum = rfft(row * window)
plt.plot(row * window)
frequency = argmax(abs(spectrum))
line_spacing = N / frequency  # pixels
print('Line spacing: {:.2f} pixels'.format(line_spacing))

plt.subplot(2, 2, 4)
plt.plot(abs(spectrum))
plt.axvline(frequency, color='r')
plt.yscale('log')
plt.show()

skew-linedetection.png

text_rotation.png