Precise Hand-printed Character Recognition Using Elastic Models via Nonlinear Transformation

Distorted character recognition is a difficult but in-evitable problem in hand-printed character recognition. In this paper, we propose a character recognition method us-ing elastic models for recognizing cursive characters with intricate structure. The models are fitted to unknown in-put patterns by applying the EM algorithm to minimize a measure of fittness. To avoid falling into local minima, mul-tiresolutional approach is introduced. Moreover, nonlinear transformation is adopted to realize more flexible matching. Experiments performed on Japanese characters show effec-tiveness of the proposed method. 1