Automated Bounding Dimension Retrieval: A Machine Learning-Driven Framework for Engineering Drawing Interpretation
2
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105006531261
Journal Title
International Conference on Cybernetics and Innovations Icci 2025
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SCOPUS
Bibliographic Citation
International Conference on Cybernetics and Innovations Icci 2025 (2025)
Suggested Citation
Sithisint V., Laoprom S., Wattanapornprom W., Chotibuth N., Nupakorn M., Satittham N., Bumrungvongsiri T. Automated Bounding Dimension Retrieval: A Machine Learning-Driven Framework for Engineering Drawing Interpretation. International Conference on Cybernetics and Innovations Icci 2025 (2025). doi:10.1109/ICCI64209.2025.10987513 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/110461
Title
Automated Bounding Dimension Retrieval: A Machine Learning-Driven Framework for Engineering Drawing Interpretation
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Abstract
Accurately retrieving bounding dimensions from engineering drawings is critical for manufacturing processes, influencing machining feasibility, material selection, and cost estimation. This paper presents an innovative Automated Bounding Dimension Retrieval (ABDR) framework, integrating state-of-the-art object detection and Optical Character Recognition (OCR) technologies with a novel ratio-based analysis methodology. The proposed multi-stage pipeline systematically identifies primary, secondary, and tertiary dimensions across multiple views, addressing challenges such as view separation, bounding box detection, and text classification. The ABDR framework was evaluated on a dataset of engineering drawings adhering to ISO standards. The view detection module, implemented using YOLOv10n, achieved 98% mAP50, while the bounding box detection module using YOLOv11n reached 98.7% mAP50 and 97.5% mAP75. The text extraction module, employing EasyOCR, attained 83.55% accuracy and 95.87% normalized edit distance. The overall pipeline achieved 81% accuracy in dimension retrieval. By automating this traditionally manual and error-prone process, the ABDR framework provides a robust and scalable solution, offering significant advancements for technical drawing interpretation and manufacturing workflow automation.