  In this study, we present a novel end-to-end approach based on the
encoder-decoder framework with the attention mechanism for online handwritten
mathematical expression recognition (OHMER). First, the input two-dimensional
ink trajectory information of handwritten expression is encoded via the gated
recurrent unit based recurrent neural network (GRU-RNN). Then the decoder is
also implemented by the GRU-RNN with a coverage-based attention model. The
proposed approach can simultaneously accomplish the symbol recognition and
structural analysis to output a character sequence in LaTeX format. Validated
on the CROHME 2014 competition task, our approach significantly outperforms the
state-of-the-art with an expression recognition accuracy of 52.43% by only
using the official training dataset. Furthermore, the alignments between the
input trajectories of handwritten expressions and the output LaTeX sequences
are visualized by the attention mechanism to show the effectiveness of the
proposed method.
