Context dependent search in interconnected hidden Markov model for unconstrained handwriting recognition

Viewing a handwritten word as an alternating sequence of characters and ligatures, we proposed a circularly interconnected network of hidden Markov models to model handwritten English words of indefinite length. The recognition problem is then regarded as finding the most probable path in the networ...

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Bibliographic Details
Published in:Pattern recognition 1995-11, Vol.28 (11), p.1693-1704
Main Authors: Oh, Se-Chang, Ha, Jin-Young, Kim, Jin-H.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Back-tracking
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Hidden Markov model
Lexicon lookup
Ligature modeling
Network search
Pattern recognition. Digital image processing. Computational geometry
Unconstrained handwriting recognition
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Summary:Viewing a handwritten word as an alternating sequence of characters and ligatures, we proposed a circularly interconnected network of hidden Markov models to model handwritten English words of indefinite length. The recognition problem is then regarded as finding the most probable path in the network for a given input. For the search, Viterbi algorithm is applied with lexicon lookup. To overcome directional sensitivity of the path search, a back-tracking technique is employed that keeps plausible path candidates dynamically within limited storage space.
ISSN:0031-3203
1873-5142
DOI:10.1016/0031-3203(95)00020-Z