# Semantic Segmentation of Node and Edge Diagrams for Assistive Technology

Source: [Semantic Segmentation of Node and Edge Diagrams for Assistive Technology](<https://www.sota2.com/research/paper/6a2a40edd33dfb23e78391a7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-12T05:00:05.419Z

Authors: Michael Cormier, Yichun Zhao, Laura Paul, Cameron Swift, Duc Tri Dang, Miguel Nacenta

Published: 2026-06-09T18:02:12.000Z

## Abstract

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\\%.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.11320>)

[PDF](<https://arxiv.org/pdf/2606.11320.pdf>)