# Semantic Segmentation of Node and Edge Diagrams for Assistive Technology

arXiv: https://arxiv.org/abs/2606.11320 · HTML: https://mlsanity.com/paper/2606.11320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [Data Extraction from Charts via Single Deep Neural Network](https://mlsanity.com/paper/1906.11906/) (2019)
- [Chart-Text: A Fully Automated Chart Image Descriptor](https://mlsanity.com/paper/1812.10636/) (2018)
- [Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline](https://mlsanity.com/paper/1907.13550/) (2019)
- [GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level](https://mlsanity.com/paper/1908.07855/) (2019)
- [ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention](https://mlsanity.com/paper/2010.00562/) (2020)
- [AutoChart: A Dataset for Chart-to-Text Generation Task](https://mlsanity.com/paper/2108.06897/) (2021)
- [Chart4Blind: An Intelligent Interface for Chart Accessibility Conversion](https://mlsanity.com/paper/2403.06693/) (2024)
- [Symbol detection in online handwritten graphics using Faster R-CNN](https://mlsanity.com/paper/1712.04833/) (2017)
- [TADA: Making Node-link Diagrams Accessible to Blind and Low-Vision People](https://mlsanity.com/paper/2311.04502/) (2023)
- [Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams](https://mlsanity.com/paper/1711.09528/) (2017)

## Abstract (arXiv)

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%.