NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition
<|reference_start|>NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition: 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.<|reference_end|>
@article{liu2024namer:,
    title={NAMER: Non-Autoregressive Modeling for Handwritten Mathematical
  Expression Recognition},
    author={Chenyu Liu, Jia Pan, Jinshui Hu, Baocai Yin, Bing Yin, Mingjun Chen,
  Cong Liu, Jun Du, Qingfeng Liu},
    journal={arXiv preprint arXiv:2407.11380},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11380},
    primaryClass={cs.CV cs.LG}
}