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\%.

翻译：本文提出了一组用于节点-连接图语义分割的新型关联模型。此类图解常被用于表示数学图论关系、概念关联及流程图，但难以通过非视觉方式访问。尽管现有部分辅助交互界面已针对节点-连接图进行设计，其运行依赖于机器的可读表示形式，而这类图解通常以位图图像形式存在。我们提出的紧凑型深度学习模型在大型合成节点-连接图数据集上展现出优异的定量与定性性能，逐像素准确率达到93%以上。