Most of the existing tree-structured decoding methods of handwritten mathematical expression recognition are based on the recurrent neural networks, which have low training efficiency and complicated training process. In order to prove this problem, the authors propose a handwritten mathematical expression recognition model based on Transformer structure, which can decode the syntax tree of expressions directly. Experimental results show that the proposed tree-structured decoding method achieves better performance than the string decoding methods base on Transformer on several datasets of handwritten formula recognition tasks, and show the potential to surpass recurrent neural network tree decoding methods.

%U https://xbna.pku.edu.cn/EN/10.13209/j.0479-8023.2023.085