摘要
针对草图识别算法大多通过限制用户绘制习惯来提高识别精确度的问题,提出一种动态构造贝叶斯网络模型的草图符号识别方法。该方法采用了从下而上与从上而下相结合的识别算法。从下而上实现笔画的分割,根据后验概率产生假设模板,继而产生图形模板。在从上而下的处理中,通过假设模板重构实现笔画重组、根据图形模板的空槽实现笔画识别的纠错处理。通过对UML领域中草图符号的识别,表明算法能在不限制用户绘制习惯的基础上取得较好的识别效果。
To solve the current algorithm＇s limitation of restricting the users＇ drawing style,this article introduced a method of dynamically constructing Bayes net to sketch symbol recognition system.This paper adopted a identifying algorithm which is a combination of bottom-top and top-bottom.From bottom to top it realizes the segmentation of strokes,generating hypothesis templates according to posterior probability then generating graphics templates.From top to bottom it realizes regrouping strokes through reconfiguring hypothesis templates and handling nosiy input according to the empty slot of templates.Through being applied to the domain of UML,we can get better recognition effect without restricting users＇ freely input.
出处
《计算机科学》
CSCD
北大核心
2011年第6期262-265,共4页
Computer Science
关键词
贝叶斯网络
先验概率
后验概率
符号识别
假设模板
Bayesian network
Prior probability
Posterior probability
Symbol recognition
Hypothesis templates
