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.

本文介绍了一种基于双分支变压器网络的可打印数学表达式识别（PMER）方法，通过学习局部和全局上下文信息来解决卷积神经网络无法捕捉到所有数学符号的问题。实验结果表明，该方法在数学表达式识别方面具有准确性且达到了最先进的性能。