  Over the past years, Printed Mathematical Expression Recognition (PMER) has
progressed rapidly. However, due to the insufficient context information
captured by Convolutional Neural Networks, some mathematical symbols might be
incorrectly recognized or missed. To tackle this problem, in this paper, a Dual
Branch transformer-based Network (DBN) is proposed to learn both local and
global context information for accurate PMER. In our DBN, local and global
features are extracted simultaneously, and a Context Coupling Module (CCM) is
developed to complement the features between the global and local contexts. CCM
adopts an interactive manner so that the coupled context clues are highly
correlated to each expression symbol. Additionally, we design a Dynamic Soft
Target (DST) strategy to utilize the similarities among symbol categories for
reasonable label generation. Our experimental results have demonstrated that
DBN can accurately recognize mathematical expressions and has achieved
state-of-the-art performance.
