  Handwritten mathematical expression recognition (HMER) is an important
research direction in handwriting recognition. The performance of HMER suffers
from the two-dimensional structure of mathematical expressions (MEs). To
address this issue, in this paper, we propose a high-performance HMER model
with scale augmentation and drop attention. Specifically, tackling ME with
unstable scale in both horizontal and vertical directions, scale augmentation
improves the performance of the model on MEs of various scales. An
attention-based encoder-decoder network is used for extracting features and
generating predictions. In addition, drop attention is proposed to further
improve performance when the attention distribution of the decoder is not
precise. Compared with previous methods, our method achieves state-of-the-art
performance on two public datasets of CROHME 2014 and CROHME 2016.
