2021年5月13日
Learning symbol relation tree for online mathematical expression recognition
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- ,
- ,
- ,
- 巻
- 13189 LNCS
- 号
- 開始ページ
- 307
- 終了ページ
- 321
- DOI
- 10.1007/978-3-031-02444-3_23
This paper proposes a method for recognizing online handwritten mathematical
expressions (OnHME) by building a symbol relation tree (SRT) directly from a
sequence of strokes. A bidirectional recurrent neural network learns from
multiple derived paths of SRT to predict both symbols and spatial relations
between symbols using global context. The recognition system has two parts: a
temporal classifier and a tree connector. The temporal classifier produces an
SRT by recognizing an OnHME pattern. The tree connector splits the SRT into
several sub-SRTs. The final SRT is formed by looking up the best combination
among those sub-SRTs. Besides, we adopt a tree sorting method to deal with
various stroke orders. Recognition experiments indicate that the proposed OnHME
recognition system is competitive to other methods. The recognition system
achieves 44.12% and 41.76% expression recognition rates on the Competition on
Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and
2016 testing sets.
expressions (OnHME) by building a symbol relation tree (SRT) directly from a
sequence of strokes. A bidirectional recurrent neural network learns from
multiple derived paths of SRT to predict both symbols and spatial relations
between symbols using global context. The recognition system has two parts: a
temporal classifier and a tree connector. The temporal classifier produces an
SRT by recognizing an OnHME pattern. The tree connector splits the SRT into
several sub-SRTs. The final SRT is formed by looking up the best combination
among those sub-SRTs. Besides, we adopt a tree sorting method to deal with
various stroke orders. Recognition experiments indicate that the proposed OnHME
recognition system is competitive to other methods. The recognition system
achieves 44.12% and 41.76% expression recognition rates on the Competition on
Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and
2016 testing sets.
- リンク情報
-
- DOI
- https://doi.org/10.1007/978-3-031-02444-3_23
- arXiv
- http://arxiv.org/abs/arXiv:2105.06084
- URL
- http://arxiv.org/abs/2105.06084v1
- URL
- http://arxiv.org/pdf/2105.06084v1 本文へのリンクあり
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85130237619&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85130237619&origin=inward
- ID情報
-
- DOI : 10.1007/978-3-031-02444-3_23
- ISSN : 0302-9743
- eISSN : 1611-3349
- ISBN : 9783031024436
- arXiv ID : arXiv:2105.06084
- SCOPUS ID : 85130237619