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

<b>Dataset Description for Figshare</b>This dataset supports the findings of the research titled:<b>"Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams"</b><b>Authors:</b> Ikenna Ekeke, Carlos Francisco Moreno-García, Eyad Elyan<br><b>Affiliation:</b> Robert Gordon University, Aberdeen, Scotland, UK<br><b>Date:</b> April 2025<b>Overview</b>The research presents an end-to-end deep learning framework combining <b>YOLOv8 object detection</b> with <b>attention mechanisms</b> to improve symbol recognition in electrical diagrams, followed by a <b>graph-based wiring algorithm</b> that automates wire routing between detected symbols. The system is tested across <b>proprietary and public datasets,</b> including:CGHD (Circuit Graph Hand-drawn Diagrams)DCD (Digital Circuit Diagrams)<b>Datasets Included</b>This data bundle includes the <b>primary data</b> 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 <b>CSV format</b> that maps the data file to the corresponding figure or table in the paper.<b>Use Cases</b>This 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.