Papers › Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Jianshu Zhang, Jun Du, Li-Rong Dai
Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols. To settle this problem, we utilize the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. We improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set. We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and save the fine-grained details that will be dropped by pooling operations. Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.
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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 | DenseWAP-MSA | ExpRate | 52.8 | #14 of 17 | Archive leaderboard | report |
| Handwritten Mathmatical Expression Recognition | CROHME 2014 | DenseWAP | ExpRate | 50.1 | #15 of 17 | Archive leaderboard | report |
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