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引用次数: 26
摘要
本文提出了一种在线手写数学表达式识别系统，并对结构分析进行了改进。本文采用上下文无关语法(Context Free Grammars, CFGs)表示微信，并采用cocke - young - kasami (CYK)算法对在线手写微信的二维结构进行解析，从符号分割、识别和结构分析等方面选择最佳解释。我们提出了一种利用两个支持向量机模型从训练模式中学习结构关系而不需要任何启发式决策的方法。我们采用笔画顺序来降低解析算法的复杂度。并对结构分析进行了修正。即使CFG在某些情况下不能解决歧义，我们的方法仍然为用户提供一个包含预期结果的候选列表。我们在CROHME 2013数据库中评估了我们的方法，并证明了我们的系统在识别率和处理时间方面的改进。
A System for Recognizing Online Handwritten Mathematical Expressions and Improvement of Structure Analysis
This paper presents a system for recognizing online handwritten mathematical expressions (MEs) and improvement of structure analysis. We represent MEs in Context Free Grammars (CFGs) and employ the Cocke-Younger-Kasami (CYK) algorithm to parse 2D structure of on-line handwritten MEs and select the best interpretation in terms of symbol segmentation, recognition and structure analysis. We propose a method to learn structural relations from training patterns without any heuristic decisions by using two SVM models. We employ stroke order to reduce the complexity of the parsing algorithm. Moreover, we revise structure analysis. Even though CFG does not resolve ambiguities in some cases, our method still gives users a list of candidates that contain expecting result. We evaluate our method in the CROHME 2013 database and demonstrate the improvement of our system in recognition rate as well as processing time.
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