Bayesian Network Modeling of Hangul Characters for On-line 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 2-D Gaussian for X-Y coordinates of point instances. Relationships between components are modeled with their positional dependencies. For on-line 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.