Abstract
In design and manufacturing engineering, the manual interpretation of 2D technical drawings remains a significant bottleneck, impeding efficiency in tasks such as part quotation and process manufacturing. While computer vision advancements have shown promise in interpreting natural images, the translation of engineering drawings into actionable data poses unique challenges. Our research proposes a novel data-driven framework that leverages computational vision and graph learning to automate the vectorization and low-level interpretation of raster engineering drawings, bringing us closer to a CAD format. Our approach involves the vectorization of rasterized engineering drawings into a collection of lines and arcs. We construct a connectivity graph from these lines and arcs and train a graph convolutional neural network to classify entity types accurately. We concentrate on 9 distinct classes: Lines, Arrowheads, Text, Dimension Lines, Center Lines, Arcs, Hidden Lines and Contours, and Misc. We achieved a notably high performance of 98.2% validation accuracy, a 20.66% improvement compared to prior methodologies, and a maximum of 46.48% increase compared to baseline learning methods. By streamlining the interpretation of technical drawings, our framework offers a pathway to enhance efficiency in modern manufacturing processes, reducing reliance on manual labor and boosting overall productivity.