NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition

• URL: http://arxiv.org/abs/2407.11380v1
• Date: Tue, 16 Jul 2024 04:52:39 GMT
• Title: NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition
• Authors: Chenyu Liu, Jia Pan, Jinshui Hu, Baocai Yin, Bing Yin, Mingjun Chen, Cong Liu, Jun Du, Qingfeng Liu,
• Abstract summary: Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding. This paper makes the first attempt to build a novel bottom-up Non-AutoRegressive Modeling approach for HMER, called NAMER. NAMER comprises a Visual Aware Tokenizer (VAT) and a Parallel Graph (PGD)
• Score: 80.22784377150465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding. Current methods typically approach HMER as an image-to-sequence generation task within an autoregressive (AR) encoder-decoder framework. However, these approaches suffer from several drawbacks: 1) a lack of overall language context, limiting information utilization beyond the current decoding step; 2) error accumulation during AR decoding; and 3) slow decoding speed. To tackle these problems, this paper makes the first attempt to build a novel bottom-up Non-AutoRegressive Modeling approach for HMER, called NAMER. NAMER comprises a Visual Aware Tokenizer (VAT) and a Parallel Graph Decoder (PGD). Initially, the VAT tokenizes visible symbols and local relations at a coarse level. Subsequently, the PGD refines all tokens and establishes connectivities in parallel, leveraging comprehensive visual and linguistic contexts. Experiments on CROHME 2014/2016/2019 and HME100K datasets demonstrate that NAMER not only outperforms the current state-of-the-art (SOTA) methods on ExpRate by 1.93%/2.35%/1.49%/0.62%, but also achieves significant speedups of 13.7x and 6.7x faster in decoding time and overall FPS, proving the effectiveness and efficiency of NAMER.

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