  Handwritten mathematical expression recognition (HMER) is a challenging task
that has many potential applications. Recent methods for HMER have achieved
outstanding performance with an encoder-decoder architecture. However, these
methods adhere to the paradigm that the prediction is made "from one character
to another", which inevitably yields prediction errors due to the complicated
structures of mathematical expressions or crabbed handwritings. In this paper,
we propose a simple and efficient method for HMER, which is the first to
incorporate syntax information into an encoder-decoder network. Specifically,
we present a set of grammar rules for converting the LaTeX markup sequence of
each expression into a parsing tree; then, we model the markup sequence
prediction as a tree traverse process with a deep neural network. In this way,
the proposed method can effectively describe the syntax context of expressions,
alleviating the structure prediction errors of HMER. Experiments on three
benchmark datasets demonstrate that our method achieves better recognition
performance than prior arts. To further validate the effectiveness of our
method, we create a large-scale dataset consisting of 100k handwritten
mathematical expression images acquired from ten thousand writers. The source
code, new dataset, and pre-trained models of this work will be publicly
available.
