用边权重图注意力模型提升手写公式识别准确率
Local and Global Graph Modeling with Edge-weighted Graph Attention Network for Handwritten Mathematical Expression Recognition
- 引入边权重图注意力机制，同时完成符号与关系分类
- 在在线手写公式识别中实现端到端的局部与全局特征建模
- 适合需要高精度公式结构理解的研究与应用
本文提出一种基于图建模的手写数学表达式识别新方法。设计了端到端的边权重图注意力网络（EGAT），可同时进行节点和边的分类，有效融合节点与边特征，用于预测数学表达式中的符号类别及其相互关系。进一步提出一种笔画级的图建模方法，分别捕获局部（LGM）和全局（GGM）信息，将在线手写公式识别任务转化为图结构中的节点与边分类问题。通过联合利用局部与全局图特征，全面理解表达式结构。实验表明，该系统在符号检测、关系分类及表达式级识别上均表现优异。
原文摘要 · Abstract (English)
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.
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