Extracting intuitive strokes in complex structured patterns with domain knowledge

A standard iterative thinning algorithm which has been widely used to extract features for character recognition may destroy information due to several defects such as spurious loops in the skeleton and deformation in touched strokes. This is because most thinning algorithms rely on the steady erosion of character boundaries while maintaining the connectivity of the shape. To solve this problem, this paper proposes a knowledge-based thinning method which removes the spurious loops by a preprocessing stage and makes use of average stroke widths and domain knowledge on Hangul (Korean script) to extract intuitive strokes. The experimental results on 2000 handwritten Hangul characters in PE92 benchmark database indicate that the proposed method has reduced the number of defects and led to more intuitive strokes.