Abstract
Recognition of handwritten mathematical expressions to 
is an image-to-sequence task. Recent research has shown that encoder-decoder models are well suited for this challenge. Many innovative models based on this structure have been proposed, especially on the decoder. Such as attention mechanism and bidirectional mutual learning are used in the decoder. And our model also improves the encoder. We use the multi-scale fusion DenseNet as the encoder and add Global Context Attention. This attention mechanism combines the advantages of force-spatial attention and channel attention. The feature maps of the two scales output by the encoder are used as inputs to the two decoder branches. The decoder uses a two-way mutual learning Transformer, which can understand high-level semantic and contextual information, and can handle long sequences of information well. In order to save memory, the two decoder branches use a set of parameters, and the last two branches are distilled and learned from each other. In this way, not only the bidirectional decoders can learn from each other, but also the two decoder branches can learn from each other, which increases the robustness of the model. Our model achieves 56.80%, 53.34% and 54.62% accuracy on CROHME2014, 2016 and 2019, respectively, and 66.22% accuracy on our own constructed dataset HME100k.
This work is partially supported by the National Natural Science Foundation of China (Nos. 61801288).
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Han, X., Liu, Q., Han, Z., Lin, Y., Xu, N. (2022). Handwritten Mathematical Expression Recognition via GCAttention-Based Encoder and Bidirectional Mutual Learning Transformer. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_22
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