Multiple Methods for Image Skew Angle Correction

This repository contains multiple methods for correcting image skew angles, including Hough Transform, Radon Transform, and conventional linear detection approaches. Additional techniques such as Hoffman Transform and edge detection algorithms can also be employed for image deskewing. These methods can be selected based on image characteristics and specific requirements to achieve optimal correction results. The correction algorithms can be implemented using image processing software or programming languages like Python with OpenCV or MATLAB's Image Processing Toolbox. Key functions include HoughLines for line detection in OpenCV, radon transform implementations for projection-based angle estimation, and edge detection operators like Canny or Sobel for preprocessing. The implementation typically involves calculating the dominant angle from detected lines or projections, then applying affine transformations using rotation matrices to correct the skew. These approaches enhance correction accuracy and efficiency by providing multiple algorithmic options suitable for different image types, from document scans to natural images with varying noise levels and structural patterns.