Bayesian network modeling of Hangul characters for online handwriting recognition
Bayesian network modeling of Hangul characters for online handwriting recognition
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用于在线手写识别的韩文字符贝叶斯网络建模
DOI：
10.1109/icdar.2003.1227660
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
J. H. Kim
中科院分区：
文献类型：
--
作者：
Sung;J. H. Kim
在本文中，我们提出了一个贝叶斯网络框架，明确建模组件和韩国韩文字符之间的关系。韩语字符采用分层组件进行建模：音节模型、字形模型、笔画模型和点模型。每个模型都是由子组件和它们之间的关系构建的，除了一个点模型，即原始模型，它由点实例的X-Y坐标的2D高斯表示。零部件之间的关系通过其位置依赖关系进行建模。对于在线手写韩文字符，该系统显示出更高的识别率比HMM系统与链码功能：95.7%比92.9%的平均水平。
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.