Abstract
Piping and Instrumentation Diagrams (P&IDs) play a crucial role in the chemical industry, yet they are often stored as scanned images or computer-aided design (CAD) drawings, limiting their seamless integration into modern digital workflows. Consequently, the task of automating P&ID digitization has attracted significant attention from the computer-aided design (CAD) research community. Traditional approaches, which typically rely on conventional object detection techniques, often demand extensive manual annotations to accurately identify and classify the various symbols in P&IDs. In this paper, we proposed a novel framework for automating the recognition of P&IDs. Our method first extracts key features of geometric primitives in CAD drawings through an automated process. Subsequently, a Transformer-based model is employed to predict the layer assignment of these primitives. Following this, an unsupervised clustering method, guided by predefined rules and geometric distances, is applied within each layer type to facilitate the automatic identification of devices, pipes, instrumentation, valves, insulation, and utilities in P&IDs. Extensive evaluation on an annotated dataset comprising 336 authentic P&IDs demonstrates that the proposed semantic annotation method achieves an overall accuracy exceeding 92%. The results of the unsupervised clustering are visualized, demonstrating promising performance and supporting the effectiveness of the proposed method. This study provides valuable insights into the intelligent digitization of P&IDs and lays the groundwork for further advancements in intelligent chemical process design.