Journal of South China University of Technology(Natural Science) >
Mutual Learning Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Feature Fusion
Received date: 2023-02-06
Online published: 2023-06-20
Supported by
the National Natural Science Foundation of China(61772048);the Natural Science Foundation of Beijing(4153058);the Construction of High Quality Undergraduate Courseware for Beijing Education Commission(040000514122506)
With complex two-dimensional structure, offline handwritten mathematical expressions is difficult to recognize due to the variable scale of their symbols and the various transformation of their writing styles. This paper proposed a mutual learning model based on multi-scale feature fusion. Firstly, to enhance the model for extracting fine-grained information from expressions and comprehending semantic information of global two-dimensional structures, multi-scale feature fusion was introduced in the encoding stage. Secondly, paired handwritten and printed mathematical expressions were introduced for training the mutual learning model, which includes decoder loss and context matching loss to learn LaTeX grammar as well as semantic invariance between handwritten and printed mathematical expressions respectively to improve the robustness of the model to different writing styles. Experimental validation was performed on the CROHME 2014/2016/2019 dataset. After introducing the multi-scale feature fusion mechanism, the expression correctness rate reaches 55.25%, 52.31%, 53.72%, respectively. After introducing the mutual learning mechanism, the expression correct rate reaches 55.43%, 53.53%, 53.79%, respectively. The expression correctness rate reaches 58.88%, 55.10%, 57.05% after introducing both mechanisms at the same time. It is proved experimentally that the proposed method can effectively extract the features in formulas at different scales and overcome the problems of different handwriting styles and small amount of data by mutual learning mechanism. In addition, the experimental results on the HME100K dataset verified the effectiveness of the proposed model.
FU Pengbin, XU Yu, YANG Huirong . Mutual Learning Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Feature Fusion[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(2) : 23 -31 . DOI: 10.12141/j.issn.1000-565X.230034
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