Vision-Based Topology-Consistent Structural Parsing of Hand-Drawn Circuit Diagrams.

Hand-drawn circuit diagrams remain common in education, maintenance, and early-stage design and are often photographed for storage, sharing, and reuse. Recovering electrically meaningful structure from such camera-acquired images is difficult because irregular strokes, wire discontinuities, crossings, symbol-text interference, and imaging artifacts can disrupt valid circuit topology. We therefore formulate the task as topology recovery with semantic completion rather than symbol recognition alone. To solve it, we propose a topology-consistent structural parsing framework that integrates multi-source visual perception, wire connected-component-guided connectivity reasoning, and explicit endpoint semantic recovery for direction-sensitive and multi-terminal components. On an independent benchmark of 1317 hand-drawn circuit diagrams, the proposed method achieves a 95.14% strict image-level end-to-end success rate. The recovered structures are further exported as Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlists for downstream simulation and verification. These results support a practical vision-based image acquisition and processing workflow for converting camera-acquired hand-drawn circuit images into machine-readable and simulation-ready circuit representations.