SINA: A Circuit Schematic Image-to-Netlist Generator Using Artificial Intelligence

Current methods for converting circuit schematic images into machine-readable netlists struggle with component recognition and connectivity inference. In this paper, we present SINA, an open-source, fully automated circuit schematic image-to-netlist generator. SINA integrates deep learning for accurate component detection, Connected-Component Labeling (CCL) for precise connectivity extraction, and Optical Character Recognition (OCR) for component reference designator retrieval, while employing a Vision-Language Model (VLM) for reliable reference designator assignments. In our experiments, SINA achieves 96.47% overall netlist-generation accuracy, which is 2.72x higher than state-of-the-art approaches.

Source: arXiv:2601.22114v1 - http://arxiv.org/abs/2601.22114v1 PDF: https://arxiv.org/pdf/2601.22114v1 Original Link: http://arxiv.org/abs/2601.22114v1