Precise hand-printed character recognition using elastic models via nonlinear transformation

Tsuyoshi Kato, Shin'ichiro Omachi, Hirotomo Aso

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)364-367
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number2
Publication statusPublished - 2000 Dec 1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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