[Submitted on 20 Oct 2026]
Title:Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis
View a PDF of the paper titled Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis, by Mohammad Kawsar and 6 other authors
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Abstract:This study proposes a sketch-to-simulation system to solve handwritten circuit diagrams. The system converts raw images into a Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlist and automatically calculates the circuit's nodal voltages and currents. Another objective is to propose a holistic framework that benefits students and educators by enhancing the teaching and learning experience of electric circuits and expediting engineering design reviews. This article presents a multistage pipeline that converts raw photographs of handwritten electric circuits into executable SPICE-like netlists, providing a solution. A dataset of 1,000 hand-drawn Resistor-Inductor-Capacitor (RLC) circuits was curated and annotated for components, text regions, and character recognition. The proposed system decomposes a circuit diagram into three stages: (i) dual You Only Look Once (YOLO)-based detectors for components and text regions, (ii) a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) and Optical Character Recognition (OCR)-based domain-aware symbol value extraction, and (iii) image processing with thresholding, skeletonization, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to automatically identify nodes and map them with corresponding components. The proposed system produces the netlist and solves the circuit. The qualitative and quantitative ablation studies on modular stages, as well as end-to-end tests, demonstrate robust performance across various handwriting styles, lighting conditions, and this http URL study serves as an interactive learning aid and practical tool for professionals, providing a comprehensive user interface (UI), enabling rapid digitization of circuit sketches for study, design, prototyping, or troubleshooting.
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.28836 [cs.AI]
(or arXiv:2610.28836v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.28836
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.7717/peerj-cs.4103
DOI(s) linking to related resources
Submission history
From: AKM Bahalul Haque [view email] [v1] Tue, 20 Oct 2026 19:04:12 UTC (20,479 KB)
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