Recognizing Handwritten Mathematical Expressions as LaTex Sequences Using a Multiscale Robust Neural Network

• URL: http://arxiv.org/abs/2003.00817v1
• Date: Wed, 26 Feb 2020 12:39:06 GMT
• Title: Recognizing Handwritten Mathematical Expressions as LaTex Sequences Using a Multiscale Robust Neural Network
• Authors: Hongyu Wang, Guangcun Shan
• Abstract summary: A robust multiscale neural network is proposed to recognize handwritten mathematical expressions and output sequences. With the addition of visualization, the model's recognition process is shown in detail. The present model results suggest that the state-of-the-art model has better robustness, fewer errors, and higher accuracy.
• Score: 3.9164573079514016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a robust multiscale neural network is proposed to recognize handwritten mathematical expressions and output LaTeX sequences, which can effectively and correctly focus on where each step of output should be concerned and has a positive effect on analyzing the two-dimensional structure of handwritten mathematical expressions and identifying different mathematical symbols in a long expression. With the addition of visualization, the model's recognition process is shown in detail. In addition, our model achieved 49.459% and 46.062% ExpRate on the public CROHME 2014 and CROHME 2016 datasets. The present model results suggest that the state-of-the-art model has better robustness, fewer errors, and higher accuracy.

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