Syntax-Aware Network for Handwritten Mathematical Expression Recognition

Understand

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.