research-article

Read Ten Lines at One Glance: Line-Aware Semi-Autoregressive Transformer for Multi-Line Handwritten Mathematical Expression Recognition

Authors:

Cong YaoAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 2066 - 2077

https://doi.org/10.1145/3581783.3612499

Published: 27 October 2023 Publication History

Abstract

Handwritten Mathematical Expression Recognition (HMER) plays a critical role in various applications, such as digitized education and scientific research. Although existing methods have achieved promising performance on publicly available datasets, they still struggle to recognize multi-line mathematical expressions (MEs), suffering from complex structures and slow inference speed. To address these issues, we propose a Line-Aware Semi-autoregressive Transformer (LAST) that treats multi-line mathematical expression sequences as two-dimensional dual-end structures. The proposed LAST utilizes a line-wise dual-end decoding strategy to decode multi-line mathematical expressions in parallel and perform dual-end decoding within each line. Specifically, we introduce a line-aware positional encoding module and a line-partitioned dual-end mask to endow LAST with line order awareness and directionality. Additionally, we adopt a shared-task optimization strategy to train LAST in both autoregressive and semi-autoregressive tasks. To evaluate the effectiveness of our approach in real-world scenarios, we have built a new Multi-line Mathematical Expression dataset (M2E), which, to the best of our knowledge, is the first of its kind and boasts with the largest character category, the largest samples of characters, and the longest average sequence length, compared to existing ME datasets. Experimental results on both the M2E dataset and publicly available datasets demonstrate the effectiveness of our proposed method. Notably, our semi-autoregressive decoding approach achieves significantly faster decoding speeds while still achieving state-of-the-art performance compared to the existing methods.

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Cited By

Zong TYang LLuo WYu WCai BZhang HLiu FYan QGao L(2024)ICPR 2024 Competition on Multi-line Mathematical Expressions RecognitionPattern Recognition. Competitions10.1007/978-3-031-80139-6_2(17-29)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-3-031-80139-6_2
Guan TLin CShen WYang X(2024)PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest TransformerComputer Vision – ECCV 202410.1007/978-3-031-72670-5_8(130-147)Online publication date: 30-Sep-2024
https://doi.org/10.1007/978-3-031-72670-5_8

Index Terms

Read Ten Lines at One Glance: Line-Aware Semi-Autoregressive Transformer for Multi-Line Handwritten Mathematical Expression Recognition
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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NSFC, Zhuhai Industry Core and Key Technology Research Project
Science and Technology Foundation of Guangzhou Huangpu Development District

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MM '23

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Zong TYang LLuo WYu WCai BZhang HLiu FYan QGao L(2024)ICPR 2024 Competition on Multi-line Mathematical Expressions RecognitionPattern Recognition. Competitions10.1007/978-3-031-80139-6_2(17-29)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-3-031-80139-6_2
Guan TLin CShen WYang X(2024)PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest TransformerComputer Vision – ECCV 202410.1007/978-3-031-72670-5_8(130-147)Online publication date: 30-Sep-2024
https://doi.org/10.1007/978-3-031-72670-5_8

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