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Bayesian network modeling of Hangul characters for online handwriting recognition

In this paper we propose a Bayesian network framework for explicitly modeling components and their relationships of Korean Hangul characters. A Hangul character is modeled with hierarchical components: a syllable model, grapheme models, stroke models and point models. Each model is constructed with subcomponents and their relationships except a point model, the primitive one, which is represented by a 2D Gaussian for X-Y coordinates of a point instances. Relationships between components are modeled with their positional dependencies. For online handwritten Hangul characters, the proposed system shows higher recognition rates than the HMM system with chain code features: 95.7% vs. 92.9% on average.