In this paper, we present a novel approach to Handwritten Mathematical Expression Recognition (HMER) by leveraging graph-based modeling techniques. We introduce an End-to-end model with an Edge-weighted Graph Attention Mechanism (EGAT), designed to perform simultaneous node and edge classification. This model effectively integrates node and edge features, facilitating the prediction of symbol classes and their relationships within mathematical expressions. Additionally, we propose a stroke-level Graph Modeling method for both local (LGM) and global (GGM) information, which applies an end-to-end model to Online HMER tasks, transforming the recognition problem into node and edge classification tasks in graph structure. By capturing both local and global graph features, our method ensures comprehensive understanding of the expression structure. Through the combination of these components, our system demonstrates superior performance in symbol detection, relation classification, and expression-level recognition.

翻译：本文提出了一种新颖的手写数学表达式识别方法，该方法利用基于图的建模技术。我们引入了一种采用边加权图注意力机制的端到端模型，旨在同时执行节点与边分类。该模型有效整合节点与边特征，促进数学表达式中符号类别及其关系的预测。此外，我们提出了一种适用于局部信息与全局信息的笔画级图建模方法，该方法将端到端模型应用于在线手写数学表达式识别任务，将识别问题转化为图结构中的节点与边分类任务。通过同时捕获局部与全局图特征，我们的方法确保了对表达式结构的全面理解。这些组件的结合使我们的系统在符号检测、关系分类和表达式级识别方面展现出卓越性能。