Global Context for Improving Recognition of Online Handwritten Mathematical Expressions

نویسندگان

چکیده

This paper presents a temporal classification method for all three subtasks of symbol segmentation, recognition and relation in online handwritten mathematical expressions (HMEs). The model is trained by multiple paths symbols spatial relations derived from the Symbol Relation Tree (SRT) representation HMEs. benefits global context deep bidirectional Long Short-term Memory network, which learns directly handwriting Connectionist Temporal Classification loss. To recognize an HME, symbol-level parse tree with Context-Free Grammar constructed, where are obtained results. We show effectiveness proposed on two latest CROHME datasets.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2021

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-030-86331-9_40