A weighted competitive learning extracting skeleton structure from character patterns with non-uniform width

In the handwritten character recognition, it is very important to extract essential structure of character patterns. Requirements for skeletonization can be summarized as follows: (a) Insensitive to irregular edge lines. (b) Nonstructure patterns are not extracted. (c) Insensitive to nonuniform line width. (d) Line information is held. In this paper, a weighted competitive learning method is proposed in order to achieve the above requirements. Regarding (a) and (b), unnecessary pattern information is removed by representing some region of the pattern using a single representative point (RP). In order to optimize the RPs, the competitive learning is employed. For the requirement (c), the region, covered by a RP, is adjusted according to the line width. The condition (d) is satisfied by connecting the RPs along the line and also through the border of the regions. Simulation results, obtained using so many kinds of distorted patterns, including digits, alphabet and Japanese Kanji, demonstrate the proposed method can extract essential skeleton structure despite of several distortions.