NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition

Recently, Handwritten Mathematical Expression Recognition (HMER) has gainedconsiderable attention in pattern recognition for its diverse applications indocument understanding. Current methods typically approach HMER as animage-to-sequence generation task within an autoregressive (AR) encoder-decoderframework. However, these approaches suffer from several drawbacks: 1) a lackof overall language context, limiting information utilization beyond thecurrent decoding step; 2) error accumulation during AR decoding; and 3) slowdecoding speed. To tackle these problems, this paper makes the first attempt tobuild a novel bottom-up Non-AutoRegressive Modeling approach for HMER, calledNAMER. NAMER comprises a Visual Aware Tokenizer (VAT) and a Parallel GraphDecoder (PGD). Initially, the VAT tokenizes visible symbols and local relationsat a coarse level. Subsequently, the PGD refines all tokens and establishesconnectivities in parallel, leveraging comprehensive visual and linguisticcontexts. Experiments on CROHME 2014/2016/2019 and HME100K datasets demonstratethat NAMER not only outperforms the current state-of-the-art (SOTA) methods onExpRate by 1.93%/2.35%/1.49%/0.62%, but also achieves significant speedups of13.7x and 6.7x faster in decoding time and overall FPS, proving theeffectiveness and efficiency of NAMER.