Generating Synthetic Handwritten Mathematical Expressions from a LaTeX Sequence or a MathML Script Collecting handwritten mathematical expressions (HMEs) generally requires a lot of time and effort for data preparation, data collection, annotation and so on. In this paper, we present a method for generating realistic HMEs in a wide variety of structures and styles from a LaTeX sequence or a MathML script using online isolated symbol patterns. Our method firstly positions all symbols in a symbol relation tree constructed from the input LaTeX or MathML script. Then, it places normalized online symbol patterns into the corresponding locations. A questionnairebased experiment shows that the synthetic patterns are as clear and natural as the real patterns. Therefore, we can use the generated synthetic HME patterns for research on HME recognition and clustering.

Multimodal Attention Network for Handwritten Mathematical Expression Recognition In this paper, we propose a novel multimodal attention network (MAN), which is based on encoderdecoder framework, for handwritten mathematical expression recognition (HMER). Here, multimodal means two specific modalities: online and offline, where online modality employs dynamic trajectories as input and offline modality employs static images as input. More specifically, the proposed method first feeds dynamic trajectories and static images into online and offline channels of the multimodal encoder respectively. The output of the encoder is then transferred to the multimodal decoder to generate a LaTeX sequence as the mathematical expression recognition result. To make full use of the complementary information that comes from the two modalities, we propose a reattention mechanism as an enhanced version of the multimodal attention mechanism which can further improve the recognition performance. Evaluated on a benchmark published by CROHME competition, the proposed approach achieves an expression recognition accuracy of 54.05% on CROHME 2014 and 50.56% on CROHME 2016 which substantially outperforms the stateofthearts using the single online or offline modality.