  Handwritten mathematical expression recognition (HMER) has attracted
extensive attention recently. However, current methods cannot explicitly study
the interactions between different symbols, which may fail when faced similar
symbols. To alleviate this issue, we propose a simple but efficient method to
enhance semantic interaction learning (SIL). Specifically, we firstly construct
a semantic graph based on the statistical symbol co-occurrence probabilities.
Then we design a semantic aware module (SAM), which projects the visual and
classification feature into semantic space. The cosine distance between
different projected vectors indicates the correlation between symbols. And
jointly optimizing HMER and SIL can explicitly enhances the model's
understanding of symbol relationships. In addition, SAM can be easily plugged
into existing attention-based models for HMER and consistently bring
improvement. Extensive experiments on public benchmark datasets demonstrate
that our proposed module can effectively enhance the recognition performance.
Our method achieves better recognition performance than prior arts on both
CROHME and HME100K datasets.
