Bayesian network modeling of strokes and their relationships for on-line handwriting recognition

In this paper, we propose a Bayesian network framework for explicitly modeling strokes and their relationships of characters. A character is modeled as a composition of stroke models, and a stroke as a composition of point models. A point is modeled with 2-D Gaussian distribution for its X– Y positi...

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Bibliographic Details
Published in:Pattern recognition 2004-02, Vol.37 (2), p.253-264
Main Authors: Cho, Sung-Jung, Kim, Jin H.
Format: Article
Language:English
Subjects:
Bayesian networks
Dependency modeling
On-line handwritten character recognition
Stroke model
Stroke relationships
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Summary:In this paper, we propose a Bayesian network framework for explicitly modeling strokes and their relationships of characters. A character is modeled as a composition of stroke models, and a stroke as a composition of point models. A point is modeled with 2-D Gaussian distribution for its X– Y position. Relationships between points and strokes are modeled as their positional dependencies. All the models and relationships are represented probabilistically in Bayesian networks. The recognition experiment with on-line handwritten digits showed promising results; the recognition errors of the proposed system were greatly reduced by dependency modeling, and its recognition rates were higher than those of previous methods.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2003.01.001