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Automatic Labeling of Handwritten Mathematical Symbols via Expression Matching

机译：通过表达式匹配自动标记手写数学符号

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摘要

Mathematical expression recognition is one of the challenging problems in the field of handwritten recognition. Public datasets are often used to evaluate and compare different computer solutions for recognition problems in several domains of applications. However, existing public datasets for handwritten mathematical expressions and symbols are still scarce both in number and in variety. Such scarcity makes large scale assessment of the existing techniques a difficult task. This paper proposes a novel approach, based on expression matching, for generating ground-truthed exemplars of expressions (and, therefore, of symbols). Matching is formulated as a graph matching problem in which symbols of input instances of a manually labeled model expression are matched to the symbols in the model. Pairwise matching cost considers both local and global features of the expression. Experimental results show achievement of high accuracy for several types of expressions, written by different users.
机译：数学表达识别是手写识别领域中具有挑战性的问题之一。公共数据集通常用于评估和比较不同计算机解决方案在多个应用程序领域中的识别问题。但是，现有的用于手写数学表达式和符号的公共数据集无论在数量上还是种类上都仍然匮乏。这种稀缺性使得对现有技术进行大规模评估成为一项艰巨的任务。本文提出了一种基于表达式匹配的新颖方法，用于生成表达式（以及因此的符号）的真实示例。匹配表述为图匹配问题，其中手动标记的模型表达式的输入实例的符号与模型中的符号匹配。成对匹配成本同时考虑了表达式的局部和全局特征。实验结果表明，由不同用户编写的几种类型的表达式具有很高的准确性。

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