A Structure-Aware and Condition-Constrained Algorithm for Text Recognition in Power Cabinets

Power cabinet OCR enables real-time grid monitoring but faces challenges absent in generic text recognition: 7.5:1 scale variation between labels and readings, tabular layouts with semantic dependencies, and electrical constraints (220 V ± 10%). We propose SACC (Structure-Aware and Condition-Constrained), an end-to-end framework integrating structural perception with domain constraints. SACC comprises (1) MAF-Detector with adaptive dilated convolutions (r∈{1,3,5}) for multi-scale text; (2) SA-ViT, combining Vision Transformer with GCN for tabular structure modeling; and (3) DCDecoder, enforcing real-time electrical constraints during decoding. Extensive experiments demonstrate SACC’s effectiveness: achieving 86.5%, 88.3%, and 83.4% character accuracy on PCSTD, YUVA EB, and ICDAR 2015 datasets, respectively, with consistent improvements over leading methods. Ablation studies confirm synergistic improvements: MAF-Detector increases recall by 12.3SACC provides a field-deployable solution achieving 30.3 ms inference on RTX 3090. The co-design of structural analysis with differentiable constraints establishes a framework for domain-specific OCR in industrial and medical applications.