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Off-Line Handwritten Word Recognition with Explicit Character Juncture Modeling
Wongyu CHO Jin H. KIM
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1995/02/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing, Computer Graphics and Pattern Recognition
handwritten word recognition, character juncture modeling, hidden Markov models, vector quantization, principal component analysis,
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In this paper, a new off-line handwritten word recognition method based on the explicit modeling of character junctures is presented. A handwritten word is regarded as a sequence of characters and junctures of four types. Hence both characters and junctures are explicitly modeled. A handwriting system employing hidden Markov models as the main statistical framework has been developed based on this scheme. An interconnection network of character and ligature models is constructed to model words of indefinite length. This model can ideally describe any form of hamdwritten words including discretely spaced words, pure cursive words, and unconstrained words of mixed styles. Also presented are efficient encoding and decoding schemes suitable for this model. The system has shown encouraging performance with a standard USPS database.