Here is a dummy code:

def radon(img):theta = np.linspace(-90., 90., 180, endpoint=False)sinogram = skimage.transform.radon(img, theta=theta, circle=True)return sinogram
# end def

I need to get the sinogram this code outputs without using skimage. But I am unable to find any implementation in python. Can you provide an implementation using only OpenCV, numpy or any other light-weight libraries?

Edit: I need this to get the dominating angle of the image. I am trying to fix the tilt before character segmentation for an OCR system. Examples are given below:

On the left side are the inputs, and on the right side are the desired output.

Edit 2: If you can provide any other ways to get this output, it will help too.

Edit 3: Some sample images: https://drive.google.com/open?id=0B2MwGW-_t275Q2Nxb3k3TGg4N1U

Well, I had a similar problem.. After spending some time googling the issue, I found a solution that worked for me. I hope it helps.

import numpy as np
import cv2from skimage.transform import radonfilename = 'your_filename'
# Load file, converting to grayscale
img = cv2.imread(filename)
I = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
h, w = I.shape
# If the resolution is high, resize the image to reduce processing time.
if (w > 640):I = cv2.resize(I, (640, int((h / w) * 640)))
I = I - np.mean(I)  # Demean; make the brightness extend above and below zero
# Do the radon transform
sinogram = radon(I)
# 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 = np.array([np.sqrt(np.mean(np.abs(line) ** 2)) for line in sinogram.transpose()])
rotation = np.argmax(r)
print('Rotation: {:.2f} degrees'.format(90 - rotation))# Rotate and save with the original resolution
M = cv2.getRotationMatrix2D((w/2, h/2), 90 - rotation, 1)
dst = cv2.warpAffine(img, M, (w, h))
cv2.imwrite('rotated.jpg', dst)

Test:
Original image:

Rotated image: (rotation degree is -9°)

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

The problem is that after rotating the image, you will get some black borders. For your case, I think it will not affect the OCR processing.