Symbol Recognition in Piping and Instrumentation Diagrams via Mask-Aware Template Matching with Spatial Similarity
Juho Han1,2, Miyoung Uhm1,2, Heejun Youn1,2, H. David Jeong3, Ghang Lee4,5
1 Yonsei University, Seoul, Republic of Korea
2 Yonsei University, Seoul, South Korea
3 Texas A&M University, Texas, United States of America.
4 Technical University of Munich, Munich, Germany
5 Yonsei university, Korea, Republic of (South Korea)
DOI: 10.35490/EC3.2026.241
Abstract: Automated symbol recognition in Piping and Instrumentation Diagrams (P&IDs) is challenging due to varying standards and complex structures where text, lines, and symbols are intermingled. This study proposes a non-learning-based framework applying extended template matching using the symbol legend. The framework extracts the Region of Interest (RoI) masks via the Convex Hull algorithm and performs matching using Intersection over Union (IoU) as a similarity metric to reflect spatial similarity. Subsequently, Intersection over Minimum (IoM)-based Non-Maximum Suppression (NMS) is introduced to suppress duplicate detections. Experiments on real-world industrial P&IDs achieved an F1 Score of 0.992, demonstrating its applicability to P&ID digitization.
Keywords: Digital Twin, P&ID Digitization, Symbol Recognition, Template Matching
