Character Relationship Refinement Network for Handwritten Mathematical Expression Recognition

Most current Handwritten Mathematical Expression Recognition (HMER) methods employ an attention-based encoder-decoder framework, which generates LaTeX sequences from the given images, following the paradigm of predicting "one-by-one". However, this paradigm may have some challenges: 1) without considering the connectivity between characters, the prior information in the prediction process will be ignored inadvertently, especially implicit information, such as " " and " ˆ ". 2) Some characters of high similarities, such as "6/b" and "o/O", will have negative effects on prediction results. To solve these issues, we propose a simple but effective Character Relationship Refinement Network (CRRN), which consists of Joint Character Learning (JCL) and Character Refinement Mask (CRM). Specifically, JCL calculates the relationship probability between characters and uses them to improve prediction accuracy. CRM takes the character confidence coefficient in a coarse-to-fine way that can reassign the weights of all characters to improve model discriminability on easily confused characters. With the collaboration of both modules, our proposed CRRN can outperform the state-of-the-art on popular datasets.