Detecting rotation and line spacing of image of page of text using Radon transform
# -*- coding: utf-8 -*- | |
""" | |
Automatically detect rotation and line spacing of an image of text using | |
Radon transform | |
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) | |
It doesn't work with black borders | |
""" | |
from __future__ import division, print_function | |
from skimage.transform import radon | |
from PIL import Image | |
from numpy import asarray, mean, array, blackman | |
import numpy | |
from numpy.fft import rfft | |
import matplotlib.pyplot as plt | |
from matplotlib.mlab import rms_flat | |
try: | |
# More accurate peak finding from | |
# https://gist.github.com/endolith/255291#file-parabolic-py | |
from parabolic import parabolic | |
def argmax(x): | |
return parabolic(x, numpy.argmax(x))[0] | |
except ImportError: | |
from numpy import argmax | |
filename = 'skew-linedetection.png' | |
# Load file, converting to grayscale | |
I = asarray(Image.open(filename).convert('L')) | |
I = I - mean(I) # Demean; make the brightness extend above and below zero | |
plt.subplot(2, 2, 1) | |
plt.imshow(I) | |
# Do the radon transform and display the result | |
sinogram = radon(I) | |
plt.subplot(2, 2, 2) | |
plt.imshow(sinogram.T, aspect='auto') | |
plt.gray() | |
# 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() |
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http://tpgit.github.io/Leptonica/skew_8c_source.html has a probably faster algorithm that shears and then sums along raster lines, scoring based on "the square of the DIFFERENCE between adjacent line sums, summed over all lines" but would be limited to finding only angles that can be produced by shearing. The rotation method can find any angle, including 90 degrees, but can't distinguish that text is upside-down. |
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Hi endolith, I have tried with my billing image to skew correction. But it is too slow to processing the image. Thanks |
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It's help for me, thank you very much. |
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https://scantailor.org/ can do this very easily |
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Also see http://dsp.stackexchange.com/a/377/29