Precise Hand-Printed Character Recognition Using Elastic Models via Nonlinear
Transformation
Tsuyoshi Kato, Shin'ichiro Omachi, and Hirotomo Aso
Proceedings 15th International Conference on Pattern Recognition (ICPR2000),
vol.2, pp.364-367, September 2000
Abstract
Distorted character recognition is a difficult but inevitable
problem in hand-printed character recognition. In
this paper, we propose a character recognition method using
elastic models for recognizing cursive characters with
intricate structure. The models are fitted to unknown input
patterns by applying the EM algorithm to minimize a
measure of fittness. To avoid falling into local minima, multiresolutional
approach is introduced. Moreover, nonlinear
transformation is adopted to realize more flexible matching.
Experiments performed on Japanese characters show effectiveness
of the proposed method.