# Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images

arXiv: https://arxiv.org/abs/2301.03155 · HTML: https://mlsanity.com/paper/2301.03155/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Automatic Information Extraction from Piping and Instrumentation Diagrams](https://mlsanity.com/paper/1901.11383/) (2019)
- [Hand-Drawn Electrical Circuit Recognition using Object Detection and Node Recognition](https://mlsanity.com/paper/2106.11559/) (2021)
- [A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2107.10373/) (2021)

## Built on by (most cited first, 2 of 2)

- [From Engineering Diagrams to Graphs: Digitizing P&IDs with Transformers](https://mlsanity.com/paper/2411.13929/) (2024)
- [Modular Graph Extraction for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2402.11093/) (2024)

## Abstract (arXiv)

Handwritten circuit diagrams from educational scenarios or historic sources usually exist on analogue media. For deriving their functional principles or flaws automatically, they need to be digitized, extracting their electrical graph. Recently, the base technologies for automated pipelines facilitating this process shifted from computer vision to machine learning. This paper describes an approach for extracting both the electrical components (including their terminals and describing texts) as well their interconnections (including junctions and wire hops) by the means of instance segmentation and keypoint extraction. Consequently, the resulting graph extraction process consists of a simple two-step process of model inference and trivial geometric keypoint matching. The dataset itself, its preparation, model training and post-processing are described and publicly available.