# Hand-Drawn Electrical Circuit Recognition using Object Detection and Node Recognition

arXiv: https://arxiv.org/abs/2106.11559 · HTML: https://mlsanity.com/paper/2106.11559/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)

## Built on by (most cited first, 5 of 5)

- [Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2301.03155/) (2023)
- [Modular Graph Extraction for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2402.11093/) (2024)
- [AITEE -- Agentic Tutor for Electrical Engineering](https://mlsanity.com/paper/2505.21582/) (2025)
- [EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions](https://mlsanity.com/paper/2602.00095/) (2026)
- [SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence](https://mlsanity.com/paper/2607.01609/) (2026)

## Abstract (arXiv)

With the recent developments in neural networks, there has been a resurgence in algorithms for the automatic generation of simulation ready electronic circuits from hand-drawn circuits. However, most of the approaches in literature were confined to classify different types of electrical components and only a few of those methods have shown a way to rebuild the circuit schematic from the scanned image, which is extremely important for further automation of netlist generation. This paper proposes a real-time algorithm for the automatic recognition of hand-drawn electrical circuits based on object detection and circuit node recognition. The proposed approach employs You Only Look Once version 5 (YOLOv5) for detection of circuit components and a novel Hough transform based approach for node recognition. Using YOLOv5 object detection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in detecting the components. The proposed method is also able to rebuild the circuit schematic with 80% accuracy with a near-real time performance of 0.33s per schematic generation.