# Automatic Information Extraction from Piping and Instrumentation Diagrams

arXiv: https://arxiv.org/abs/1901.11383 · HTML: https://mlsanity.com/paper/1901.11383/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams](https://mlsanity.com/paper/2109.03794/) (2021)
- [From Engineering Diagrams to Graphs: Digitizing P&IDs with Transformers](https://mlsanity.com/paper/2411.13929/) (2024)
- [Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins](https://mlsanity.com/paper/2104.01854/) (2021)
- [Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models](https://mlsanity.com/paper/2603.24629/) (2026)
- [OSSR-PID: One-Shot Symbol Recognition in P&ID Sheets using Path Sampling and GCN](https://mlsanity.com/paper/2109.03849/) (2021)
- [Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2301.03155/) (2023)
- [Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams](https://mlsanity.com/paper/2108.13912/) (2021)
- [An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment](https://mlsanity.com/paper/2602.15584/) (2026)
- [Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs](https://mlsanity.com/paper/2609.05880/) (2026)

## Abstract (arXiv)

One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. These files need to be digitized for purposes of inventory management and updation, and easy reference to different components of the schematics. There are several challenging vision problems associated with digitizing real world P&ID diagrams. Real world P&IDs come in several different resolutions, and often contain noisy textual information. Extraction of instrumentation information from these diagrams involves accurate detection of symbols that frequently have minute visual differences between them. Identification of pipelines that may converge and diverge at different points in the image is a further cause for concern. Due to these reasons, to the best of our knowledge, no system has been proposed for end-to-end data extraction from P&ID diagrams. However, with the advent of deep learning and the spectacular successes it has achieved in vision, we hypothesized that it is now possible to re-examine this problem armed with the latest deep learning models. To that end, we present a novel pipeline for information extraction from P&ID sheets via a combination of traditional vision techniques and state-of-the-art deep learning models to identify and isolate pipeline codes, pipelines, inlets and outlets, and for detecting symbols. This is followed by association of the detected components with the appropriate pipeline. The extracted pipeline information is used to populate a tree-like data-structure for capturing the structure of the piping schematics. We evaluated proposed method on a real world dataset of P&ID sheets obtained from an oil firm and have obtained promising results.