Hough transform based fast skew detection and accurate skew correction methods

The Hough transform provides a robust technique for skew detection in document images, but suffers from high time complexity which becomes prohibitive for detecting skew in large documents. Analysis of time complexity on various stages of skew detection process is carried out in this paper. A complete skew detection and correction process is divided into three parts: a preprocessing stage using a simplified form of block adjacency graph (BAG), voting process using the Hough transform and de-skewing the image using rotation. Skew correction phase, which is hitherto a neglected area, is analysed for the quality of de-skewed images with respect to the type of rotation. Fast algorithms for all the three stages are presented and exhaustive analysis on time complexity is conducted. It is shown that the overall time taken for the whole process is less than one second even for very large documents. It is also observed that time taken in rotation is as significant as in skew detection which is reduced with the help of fast algorithms using integer operations. While the BAG algorithm is found to be effective for documents with Roman script, it does not provide satisfactory results for Indian scripts where headline is a part of a script.