Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition

Authors

  • Jin-Wen Wu National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
  • Fei Yin National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences
  • Yan-Ming Zhang National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences
  • Xu-Yao Zhang National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
  • Cheng-Lin Liu National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences CAS Center for Excellence of Brain Science and Intelligence Technology

Keywords:

Language and Vision, Generation, Education

Abstract

Recent handwritten mathematical expression recognition (HMER) approaches treat the problem as an image-to-markup generation task where the handwritten formula is translated into a sequence (e.g. LaTeX). The encoder-decoder framework is widely used to solve this image-to-sequence problem. However, (i) for structured mathematical formula, the hierarchical structure neither in the formula nor in the markup has been explored adequately. In addition, (ii) existing image-to-markup methods could not explicitly segment mathematical symbols in the formula corresponding to each target markup token. In this paper, we address the above issues by formulating the HMER as a graph-to-graph (G2G) learning problem. Graph is more flexible and general for structure representation and learning compared with image or sequence. At the core of our method lies the embedding of input formula and output markup into graphs on primitives, with Graph Neural Networks (GNN) to explore the structural information, and a novel sub-graph attention mechanism to match primitives in the input and output graphs. We conduct extensive experiments on CROHME datasets to demonstrate the benefits of the proposed G2G model. Our method yields significant improvements over previous SOTA image-to-markup systems. Moreover, it explicitly resolves the symbol segmentation problem while still being trained end-to-end, making the whole system much more accurate and interpretable.

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Published

2021-05-18

How to Cite

Wu, J.-W., Yin, F., Zhang, Y.-M., Zhang, X.-Y., & Liu, C.-L. (2021). Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 2925-2933. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16399

Issue

Section

AAAI Technical Track on Computer Vision III