Relation-Based Representation for Handwritten Mathematical Expression Recognition
Relation-Based Representation for Handwritten Mathematical Expression Recognition
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DOI：
10.1007/978-3-030-86198-8_1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Thanh-Nghia Truong;Quang Huy Ung;Hung Tuan Nguyen;C. Nguyen;M. Nakagawa
中科院分区：
文献类型：
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作者：
Thanh-Nghia Truong;Quang Huy Ung;Hung Tuan Nguyen;C. Nguyen;M. Nakagawa
本文提出了基于关系的序列表示，增强离线手写数学表达式（HME）的识别。通常，基于LaTeX的序列将HME的2D结构表示为1D序列。因此，基于LaTeX的序列变得更长，并且HME识别系统难以提取其2D结构。我们提出了一种新的表示HME根据符号的关系，这缩短了基于LaTeX的表示。我们使用一个离线的端到端HME识别系统，采用弱监督学习来评估所提出的表示。识别实验表明，所提出的基于关系的表示有助于HME识别系统实现更高的性能比基于LaTeX的表示。事实上，HME识别系统在2014年、2016年和2019年在线手写数学表达式识别竞赛（CROHME）的数据集上分别实现了53.35%、52.14%和53.13%的识别率。这些结果比基于LaTeX的系统高出2个百分点以上。
This paper proposes relation-based sequence representation that enhances offline handwritten mathematical expressions (HMEs) recognition. Commonly, a LaTeX-based sequence represents the 2D structure of an HME as a 1D sequence. Consequently, the LaTeX-based sequence becomes longer, and HME recognition systems have difficulty in extracting its 2D structure. We propose a new representation for HMEs according to the relations of symbols, which shortens the LaTeX-based representation. We use an offline end-to-end HME recognition system that adopts weakly supervised learning to evaluate the proposed representation. Recognition experiments indicate that the proposed relation-based representation helps the HME recognition system achieve higher performance than the LaTeX-based representation. In fact, the HME recognition system achieves recognition rates of 53.35%, 52.14%, and 53.13% on the dataset of the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016, and 2019, respectively. These results are more than 2 percentage points higher than the LaTeX-based system.