MMHMER：用于手写数学表达式识别的多视角与多任务方法
MMHMER:Multi-viewer and Multi-task for Handwritten Mathematical Expression Recognition
AI总结：
针对手写数学表达式识别中CNN/RNN与Transformer架构各有优劣的问题，提出MMHMER多视角多任务方法，融合两者优势，在CROHME数据集上性能优于Posformer，提升识别效果。
摘要：
手写数学表达式识别（HMER）方法已取得显著进展，现有多数HMER方法基于混合CNN/RNN与GRU架构或Transformer架构，两者各有优劣。将不同模型结构作为视角并有效整合其多样化能力是一个有趣的探索方向，这涉及两个关键挑战：1）如何有效融合这两种方法；2）如何在适当复杂度水平下实现更高性能。本文提出一种高效的CNN-Transformer多视角、多任务方法以提升模型识别性能。我们的MMHMER模型在CROHME14、CROHME16和CROHME19上的ExpRate分别达到63.96%、62.51%和65.46%，相比Posformer绝对增益为1.28%、1.48%和0.58%。本方法的主要贡献在于提出一种新的多视角、多任务框架，可有效整合CNN和Transformer的优势，通过利用CNN的特征提取能力和Transformer的序列建模能力，模型能更好地处理手写数学表达式的复杂性。
英文摘要：
Handwritten Mathematical Expression Recognition (HMER) methods have made remarkable progress, with most existing HMER approaches based on either a hybrid CNN/RNN-based with GRU architecture or Transformer architectures. Each of these has its strengths and weaknesses. Leveraging different model structures as viewers and effectively integrating their diverse capabilities presents an intriguing avenue for exploration. This involves addressing two key challenges: 1) How to fuse these two methods effectively, and 2) How to achieve higher performance under an appropriate level of complexity. This paper proposes an efficient CNN-Transformer multi-viewer, multi-task approach to enhance the model's recognition performance. Our MMHMER model achieves 63.96%, 62.51%, and 65.46% ExpRate on CROHME14, CROHME16, and CROHME19, outperforming Posformer with an absolute gain of 1.28%, 1.48%, and 0.58%. The main contribution of our approach is that we propose a new multi-view, multi-task framework that can effectively integrate the strengths of CNN and Transformer. By leveraging the feature extraction capabilities of CNN and the sequence modeling capabilities of Transformer, our model can better handle the complexity of handwritten mathematical expressions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
