Dual Branch Network Towards Accurate Printed Mathematical Expression Recognition
This addresses the issue of incorrect or missed symbol recognition in PMER, which is important for applications like document digitization, but it appears incremental as it builds on existing transformer-based methods.
The paper tackled the problem of insufficient context information in Printed Mathematical Expression Recognition (PMER) by proposing a Dual Branch transformer-based Network (DBN) that learns local and global features, achieving state-of-the-art performance.
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.