将节点连线图转为可读文本，让视障者也能理解数学图示。
Semantic Segmentation of Node and Edge Diagrams for Assistive Technology
- 用轻量级深度学习模型分割节点和连线
- 在合成数据集上像素准确率达93%以上
- 适合开发无障碍数学辅助工具
本文提出一套新型深度学习模型，用于对节点-连线图进行语义分割。这类图常用于表示数学图、概念关系和流程图，但对视障用户不友好。现有辅助系统依赖机器可读的结构化数据，而实际提供的往往是位图图像。本研究的紧凑模型在大规模合成节点-连线图数据集上表现优异，像素级准确率超过93%，具备良好的定量与定性效果。
原文摘要 · Abstract (English)
In this paper, we present a novel set of related models for semantic segmentation of node-link diagrams. These diagrams are frequently used to represent mathematical graphs, relationships between concepts, and flowcharts. Such diagrams are difficult to access non-visually; while some assistive interfaces have been designed for node-link diagrams, they rely upon a machine-readable representation of the diagram, whereas such diagrams will generally be made available as bitmap images. Our compact deep learning models show excellent quantitative and qualitative performance on a large synthetic dataset of node-link diagrams, reaching per-pixel accuracy over 93\%.
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