  Encoder-decoder models have made great progress on handwritten mathematical
expression recognition recently. However, it is still a challenge for existing
methods to assign attention to image features accurately. Moreover, those
encoder-decoder models usually adopt RNN-based models in their decoder part,
which makes them inefficient in processing long $\LaTeX{}$ sequences. In this
paper, a transformer-based decoder is employed to replace RNN-based ones, which
makes the whole model architecture very concise. Furthermore, a novel training
strategy is introduced to fully exploit the potential of the transformer in
bidirectional language modeling. Compared to several methods that do not use
data augmentation, experiments demonstrate that our model improves the ExpRate
of current state-of-the-art methods on CROHME 2014 by 2.23%. Similarly, on
CROHME 2016 and CROHME 2019, we improve the ExpRate by 1.92% and 2.28%
respectively.
