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Sketch2Sim: An end-to-end modular framework for hand-drawn circuit analysis

artificial-intelligence
computer-education
computer-vision
data-mining-and-machine-learning
neural-networks

Abstract

Context: The digitization of handwritten circuits plays a crucial role in the education and engineering domains. They can help analyze circuits and provide feedback that enables students, researchers, educators, and other stakeholders.
Additionally, due to the spread of digital platforms in education, handwritten circuit digitization can help students and educators in various developing countries who lack access to fully digitized content. However, this topic has remained underexplored due to data scarcity, lack of datasets, and the complexity of processing.
Objective: This study proposes a sketch-to-simulation system to solve handwritten circuit diagrams. The system converts raw images into a SPICE-compatible netlist and automatically calculates the nodal voltages and currents of the circuit.
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.
Methods: This paper 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 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 YOLO-based detectors for components and text regions, (ii) a CRNN with CTC-based domain-aware symbol value extraction, and (iii) image processing with thresholding, skeletonization, and DBSCANs to automatically identify nodes and map them with corresponding components.
Results: 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 clutter.
Conclusion: This study serves as an interactive learning aid and practical tool for professionals, enabling rapid digitization of circuit sketches for study, design, prototyping, or troubleshooting.
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