A fast and accurate skew detection algorithm

Skew detection of digital documents is one of the important preprocessing steps of document layout analysis and optical character recognition, because it affects the quality and speed of these algorithms. In literature, there is a variety of algorithms and studies. Three of these algorithms are popular, namely i) projection profile, ii) Hough transform and iii) nearest neighbor clustering. In this thesis, we propose a novel fast and accurate skew detection method. The proposed method forms rectangular blocks and uses the centroids of blocks to estimate the skew angle. Unlike other methods, the output of the proposed algorithm is a deskewed document. In other words, no additional skew correction step is required. Moreover, it is independent of the script used in the document. Having a wide range of scan interval is another advantage of the proposed method over other methods. Tests made on 180 open access images give us promising results in terms of speed and accuracy. In comparison with the most popular methods, beyond being the fastest, the proposed algorithm gives the second most accurate results