Papers › Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer
Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer
Wenqi Zhao, Liangcai Gao, Zuoyu Yan, Shuai Peng, Lin Du, Ziyin Zhang
Encoder-decoder models have made great progress on handwritten mathematical expression recognition recently. However, it is still a challenge for existing methods to assign attention to image features accurately. Moreover, those encoder-decoder models usually adopt RNN-based models in their decoder part, which makes them inefficient in processing long LaTeX sequences. In this paper, a transformer-based decoder is employed to replace RNN-based ones, which makes the whole model architecture very concise. Furthermore, a novel training strategy is introduced to fully exploit the potential of the transformer in bidirectional language modeling. Compared to several methods that do not use data augmentation, experiments demonstrate that our model improves the ExpRate of current state-of-the-art methods on CROHME 2014 by 2.23%. Similarly, on CROHME 2016 and CROHME 2019, we improve the ExpRate by 1.92% and 2.28% respectively.
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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 | BTTR | ExpRate | 53.96 | #12 of 17 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2016 | BTTR | ExpRate | 52.31 | #12 of 16 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2019 | BTTR | ExpRate | 52.96 | #12 of 14 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | HME100K | BTTR | ExpRate | 64.1 | #10 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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