TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition

16 Aug 2024  ·  Jianhua Zhu, Wenqi Zhao, Yu Li, Xingjian Hu, Liangcai Gao ·

Handwritten Mathematical Expression Recognition (HMER) has extensive applications in automated grading and office automation. However, existing sequence-based decoding methods, which directly predict $\LaTeX$ sequences, struggle to understand and model the inherent tree structure of $\LaTeX$ and often fail to ensure syntactic correctness in the decoded results. To address these challenges, we propose a novel model named TAMER (Tree-Aware Transformer) for handwritten mathematical expression recognition. TAMER introduces an innovative Tree-aware Module while maintaining the flexibility and efficient training of Transformer. TAMER combines the advantages of both sequence decoding and tree decoding models by jointly optimizing sequence prediction and tree structure prediction tasks, which enhances the model's understanding and generalization of complex mathematical expression structures. During inference, TAMER employs a Tree Structure Prediction Scoring Mechanism to improve the structural validity of the generated $\LaTeX$ sequences. Experimental results on CROHME datasets demonstrate that TAMER outperforms traditional sequence decoding and tree decoding models, especially in handling complex mathematical structures, achieving state-of-the-art (SOTA) performance.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Handwritten Mathmatical Expression Recognition CROHME 2014 TAMER ExpRate 61.23 # 1
Handwritten Mathmatical Expression Recognition CROHME 2016 TAMER ExpRate 60.26 # 2
Handwritten Mathmatical Expression Recognition CROHME 2019 TAMER ExpRate 61.97 # 2
Handwritten Mathmatical Expression Recognition HME100K TAMER ExpRate 68.52 # 3

Methods