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# endolith/output.png

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Detecting rotation and line spacing of image of page of text using Radon transform
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 """ 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 skimage.transform import radon from PIL import Image from numpy import asarray, mean, array, blackman import numpy as np from numpy.fft import rfft import matplotlib.pyplot as plt 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, np.argmax(x))[0] except ImportError: from numpy import argmax def rms_flat(a): """ Return the root mean square of all the elements of *a*, flattened out. """ return np.sqrt(np.mean(np.abs(a) ** 2)) 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()

### endolith commented May 24, 2014 • edited

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.

### yasinrawther commented Feb 8, 2017

Hi endolith,

I have tried with my billing image to skew correction. But it is too slow to processing the image.
And it is take around 230 seconds.
How can I resolve this issue?

Thanks

### tangshao0804 commented May 6, 2019

It's help for me, thank you very much.

### endolith commented May 6, 2019

https://scantailor.org/ can do this very easily

### shyam-yugen commented Jul 25, 2023

rms_flat has been deprecated since 3.1.0 of matplotlib. link here

### ablanco1950 commented Jul 25, 2023

Thanks for notice. I use this routine in several projects but without using matplolib, so I think it won't affect me.

### endolith commented Jul 25, 2023

Yep. Please comment on these scipy issues: scipy/scipy#16179 scipy/scipy#16189

### zoldaten commented Oct 4, 2023

what to replace rms_flat code ?

### endolith commented Oct 4, 2023 • edited

@zoldaten I updated the script. Still works:

### zoldaten commented Oct 5, 2023 • edited

@endolith thanks !
1.
i see output on skew-linedetection.png:

Rotation: 5.00 degrees
Line spacing: 13.63 pixels

is it correct ?

and sometimes got this:

/home/pi/.local/lib/python3.9/site-packages/skimage/transform/radon_transform.py:75: UserWarning: Radon transform: image must be zero outside the reconstruction circle
warn('Radon transform: image must be zero outside the '
Rotation: 3.00 degrees
/home/pi/Desktop/rotation_detection.py:69: RuntimeWarning: divide by zero encountered in long_scalars
line_spacing = N / frequency  # pixels
Line spacing: inf pixels

i saw remark It doesn't work with black borders
what does it mean ? do you have an example image ?

### endolith commented Oct 5, 2023

@zoldaten That's what I get for the example image, yes:

Rotation: 5.00 degrees
Line spacing: 13.63 pixels

### zoldaten commented Oct 5, 2023

@endolith
if you dont mind i ll speed up a bit your code.
now i have time 0:00:01.228 sec (with skew-linedetection.png). on raspberry pi.
the bigger pic i use the more inference time. on 2MiB pic i have already 11 sec.
i used cprofile and found that sinogram = radon(I) eats all time.
to speed up it we need smaller image.

so. we need to replace:
I = asarray(Image.open(filename).convert('L'))
with this:

import sys
from PIL.Image import Resampling

I = Image.open(filename).convert('L')
I.thumbnail([sys.maxsize, 480], Resampling.LANCZOS)  #resize image keeping aspect ratio. 480 by example. it may be smaller i think.

now i have on skew-linedetection.png:
0:00:00.739

i didnt tested how the last code works as i need only rotation degrees. and it returns the result.