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

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 Simulation Program withIntegrated Circuit Emphasis (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 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. 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, providing a comprehensive user interface (UI), enabling rapid digitization of circuit sketches for study, design, prototyping, or troubleshooting.