Abstract
Handwritten mathematical expression recognition, which can be classified into offline handwritten mathematical expression recognition and online handwritten mathematical expression recognition (OHMER) is an important research filed of pattern recognition, which has attracted extensive studies during the past years. With the emergence of deep learning, new breakthrough progresses of handwritten mathematical expression recognition have been obtained in recent years. In this paper, we review the applications of deep learning models in the field of OHMER. First, the research background and current state-of-the-art OHMER technologies as well as the major problems are introduced. Then, OHMER systems based on different deep learning methods such as CNN-based system, RNN-based system, encoder-decoder approach and so on are introduced in detail including the summaries of technical details and experiment results. Finally, further research directions are discussed.
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