Papers › NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition
NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition
Chenyu Liu, Jia Pan, Jinshui Hu, BaoCai Yin, Bing Yin, Mingjun Chen, Cong Liu, Jun Du, Qingfeng Liu
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Handwritten Mathmatical Expression Recognition | CROHME 2014 | NAMER | ExpRate | 60.51 | #5 of 17 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2016 | NAMER | ExpRate | 60.24 | #4 of 16 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2019 | NAMER | ExpRate | 61.72 | #4 of 14 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | HME100K | NAMER | ExpRate | 68.52 | #4 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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