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

Guangcun Shan; Hongyu Wang

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

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arxiv 2003.00817 v1 pith:SDANNFG7 submitted 2020-02-26 cs.CV eess.IV

Hongyu Wang , Guangcun Shan This is my paper · ORCID

classification cs.CVeess.IV

keywords mathematicalmodelexpressionshandwrittenadditioncrohmelatexmultiscale

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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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Automated LaTeX Code Generation from Handwritten Math Expressions Using Vision Transformer
cs.CV 2024-12 reject novelty 3.0 of 10

A vision transformer encoder with a transformer decoder beats small CNN-LSTM and ResNet-LSTM baselines on image-to-LaTeX conversion in the authors' reported experiments.

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