  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.
