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In this study, we present a novel end-to-end approach based on theencoder-decoder framework with the attention mechanism for online handwrittenmathematical expression recognition (OHMER). First, the input two-dimensionalink trajectory information of handwritten expression is encoded via the gatedrecurrent unit based recurrent neural network (GRU-RNN). Then the decoder isalso implemented by the GRU-RNN with a coverage-based attention model. Theproposed approach can simultaneously accomplish the symbol recognition andstructural analysis to output a character sequence in LaTeX format. Validatedon the CROHME 2014 competition task, our approach significantly outperforms thestate-of-the-art with an expression recognition accuracy of 52.43% by onlyusing the official training dataset. Furthermore, the alignments between theinput trajectories of handwritten expressions and the output LaTeX sequencesare visualized by the attention mechanism to show the effectiveness of theproposed method.

A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition