Data supporting figures and tables in Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams

Dataset Description for FigshareThis dataset supports the findings of the research titled:"Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams"Authors: Ikenna Ekeke, Carlos Francisco Moreno-García, Eyad ElyanAffiliation: Robert Gordon University, Aberdeen, Scotland, UKDate: April 2025OverviewThe research presents an end-to-end deep learning framework combining YOLOv8 object detection with attention mechanisms to improve symbol recognition in electrical diagrams, followed by a graph-based wiring algorithm that automates wire routing between detected symbols. The system is tested across proprietary and public datasets, including:CGHD (Circuit Graph Hand-drawn Diagrams)DCD (Digital Circuit Diagrams)Datasets IncludedThis data bundle includes the primary data used to generate figures and tables within the manuscript:Model performance metrics across different attention modules (Table 1 - Table 3).Class distribution tables (used in Figures 11a, 11b, 12a, 12b).Class-wise accuracy table (Table 4).Results of statistical analysis (Table 5, Duncan’s test).Validation Results on CGHD and DCD_Datasets (Table 6).Wiring algorithm evaluation metrics (Table 7).Each table is provided in CSV format that maps the data file to the corresponding figure or table in the paper.Use CasesThis dataset is useful for:Benchmarking attention-based models for electrical symbol recognition.Studying the impact of class imbalance on detection accuracy.Reproducing automated wiring design evaluations using pathfinding algorithms.