Global Context for improving recognition of Online Handwritten Mathematical Expressions

Autor:	Nguyen, Cuong Tuan, Truong, Thanh-Nghia, Nguyen, Hung Tuan, Nakagawa, Masaki
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs). The classification model is trained by multiple paths of symbols and spatial relations derived from the Symbol Relation Tree (SRT) representation of HMEs. The method benefits from global context of a deep bidirectional Long Short-term Memory network, which learns the temporal classification directly from online handwriting by the Connectionist Temporal Classification loss. To recognize an online HME, a symbol-level parse tree with Context-Free Grammar is constructed, where symbols and spatial relations are obtained from the temporal classification results. We show the effectiveness of the proposed method on the two latest CROHME datasets. Comment: 16 pages, ICDAR2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.10156 Zobrazit plný text záznamu View this record from Arxiv