From Engineering Diagrams to Graphs: Digitizing P&IDs with Transformers

TL;DR

This paper introduces a transformer-based method called Relationformer for digitizing P&ID engineering diagrams, jointly extracting symbols and their connections, and provides a new benchmark dataset showing significant performance improvements over previous methods.

Contribution

It presents the first transformer-based approach for joint symbol and connection extraction in P&IDs and introduces a publicly available benchmark dataset for this task.

Findings

Over 25% improvement in edge detection accuracy.

Transformer models outperform modular baseline methods.

Provides a reproducible evaluation framework.

Abstract

Digitizing engineering diagrams like Piping and Instrumentation Diagrams (P&IDs) plays a vital role in maintainability and operational efficiency of process and hydraulic systems. Previous methods typically decompose the task into separate steps such as symbol detection and line detection, which can limit their ability to capture the structure in these diagrams. In this work, a transformer-based approach leveraging the Relationformer that addresses this limitation by jointly extracting symbols and their interconnections from P&IDs is introduced. To evaluate our approach and compare it to a modular digitization approach, we present the first publicly accessible benchmark dataset for P&ID digitization, annotated with graph-level ground truth. Experimental results on real-world diagrams show that our method significantly outperforms the modular baseline, achieving over 25% improvement in edge detection accuracy. This research contributes a reproducible evaluation framework and demonstrates the effectiveness of transformer models for structural understanding of complex engineering diagrams. The dataset is available under https://zenodo.org/records/14803338.