A Layered Framework for Universal Extraction and Recognition of Electrical Diagrams

Secondary systems in electrical engineering often rely on traditional CAD software (AutoCAD v2024.1.6) or non-structured, paper-based diagrams for fieldwork, posing challenges for digital transformation. Electrical diagram recognition technology bridges this gap by converting traditional diagram operations into a “digital” model, playing a critical role in power system scheduling, operation, and maintenance. However, conventional recognition methods, which primarily rely on partition detection, face significant limitations such as poor adaptability to diverse diagram styles, interference among recognition objects, and reduced accuracy in handling complex and varied electrical diagrams. This paper introduces a novel layered framework for electrical diagram recognition that sequentially extracts the element layer, text layer, and connection relationship layer to address these challenges. First, an improved YOLOv7 model, combined with a multi-scale sliding window strategy, is employed to accurately segment large and small diagram objects. Next, PaddleOCR, trained with electrical-specific terminology, and PaddleClas, using multi-angle classification, are utilized for robust text recognition, effectively mitigating interference from diagram elements. Finally, clustering and adaptive FcF-inpainting algorithms are applied to repair the connection relationship layer, resolving local occlusion issues and enhancing the overall coupling of the diagram. Experimental results demonstrate that the proposed method outperforms existing approaches in robustness and universality, particularly for complex diagrams, providing technical support for intelligent power grid construction and operation.