AI

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

Read full article at source ↗

Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 04:03:24 UTC.

Briefings

A new framework named Sketch2Sim converts handwritten circuit sketches into executable SPICE netlists and calculates nodal voltages and currents. The system uses a multistage pipeline with YOLO detectors, CRNN-OCR, and DBSCAN to identify components and map them. A dataset of 1,000 annotated RLC circuits was used for training, demonstrating robust performance across various handwriting styles and lighting conditions.

Key points

• The Sketch2Sim framework converts raw handwritten circuit images into SPICE-compatible netlists and automatically calculates nodal voltages and currents.
• The system utilizes a three-stage pipeline involving YOLO-based component detection, CRNN-OCR for symbol value extraction, and DBSCAN for node mapping.

Context

This research provides a solution for digitizing hand-drawn electric circuits, offering a tool for engineering design reviews, prototyping, and troubleshooting. It serves as an interactive learning aid for students and educators by enhancing the teaching and learning experience of electric circuits.

Limitations

The study relies on a curated dataset of 1,000 RLC circuits, which may limit generalizability to other circuit types or handwriting styles. Performance was tested across various handwriting styles, lighting conditions, and clutter, but specific failure modes for other component types remain unverified.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 22/10/2026, 04:10:32 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

View archived briefing on IPFS
bafybeiao7xozzxbforohcqdcp4eojkbbj4ajtmajz7q76wgpm4s4hwxure