Small Eigenvalue Based Skew Estimation of Handwritten Devanagari Words

In this work, a novel technique for estimating skew angle in handwritten Devanagari words is proposed. Orientation of the Shirorekha, a horizontal line present at the top and touching all the characters of a word, is used as a clue to identify its skew. The method exploits the eigenvalue analysis of the covariance matrix formed by the edge pixels of the word image over a small connected region of support for the purpose extracting straight line segments. The line segments thus obtained are grouped according to their orientations. The orientation of a line segment is computed as a function of angles of its associated edge pixels. The angle of an edge pixel is identified using the eigenvector corresponding to the small eigenvalue associated with it. The line segments of each group are processed to locate a longest connected line which is decided to be the Shirorekha. The method is very fast when compared to Hough transform based line detection approach in addition to being robust to noise. Performance of the method is studied on a dataset consisting of 400 word images extracted from handwritten Devanagari documents especially of Hindi language with arbitrary orientations ranging from −45° to 45° under different scaling.