Robust Detection of Line Numbers in Piping and Instrumentation Diagrams (P&IDs)

The success of any Engineering, Procurement, and Construction (EPC) project depends on the engineering deliverables developed during project execution. An important deliverable is the Line List document, produced by extracting pipeline numbers from Piping and Instrumentation Diagrams (P&IDs). As the creation of this document is time-consuming, the automation of this process could reduce manual engineering work. However, the complexity of the P&IDs renders traditional computer vision approaches unsuitable. Therefore, deep learning text detection could be utilized to achieve this task. This study assessed the applicability of text detection methods for automating pipeline number information extraction in P&IDs. Our findings indicate that the methods previously used to detect text on P&IDs have limitations in accurately capturing the entire line numbers. Furthermore, we propose a line number detection method achieving a recall rate of over 90% on our evaluation data, consisting of P&IDs from diverse industrial projects. Thus, we demonstrate our method's generalizability to different line number formats and its potential for industrial application. Moreover, the proposed method can be adapted to other types of engineering drawings beyond P&IDs. Thus, it could be used in additional applications for digitizing engineering drawings.