# Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams

arXiv: https://arxiv.org/abs/2109.03794 · HTML: https://mlsanity.com/paper/2109.03794/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Detecting Text in Natural Image with Connectionist Text Proposal Network](https://mlsanity.com/paper/1609.03605/) (2016)
- [Automatic Information Extraction from Piping and Instrumentation Diagrams](https://mlsanity.com/paper/1901.11383/) (2019)
- [Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization](https://mlsanity.com/paper/2003.09150/) (2020)

## Built on by (most cited first, 6 of 6)

- [From Engineering Diagrams to Graphs: Digitizing P&IDs with Transformers](https://mlsanity.com/paper/2411.13929/) (2024)
- [Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models](https://mlsanity.com/paper/2603.24629/) (2026)
- [Advanced Integration of Discrete Line Segments in Digitized P&ID for Continuous Instrument Connectivity](https://mlsanity.com/paper/2505.11976/) (2025)
- [Visual Language Model as a Judge for Object Detection in Industrial Diagrams](https://mlsanity.com/paper/2510.03376/) (2025)
- [SynthPID: P&ID digitization from Topology-Preserving Synthetic Data](https://mlsanity.com/paper/2604.16513/) (2026)
- [Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs](https://mlsanity.com/paper/2609.05880/) (2026)

## Abstract (arXiv)

Digitization of scanned Piping and Instrumentation diagrams(P&ID), widely used in manufacturing or mechanical industries such as oil and gas over several decades, has become a critical bottleneck in dynamic inventory management and creation of smart P&IDs that are compatible with the latest CAD tools. Historically, P&ID sheets have been manually generated at the design stage, before being scanned and stored as PDFs. Current digitization initiatives involve manual processing and are consequently very time consuming, labour intensive and error-prone.Thanks to advances in image processing, machine and deep learning techniques there are emerging works on P&ID digitization. However, existing solutions face several challenges owing to the variation in the scale, size and noise in the P&IDs, sheer complexity and crowdedness within drawings, domain knowledge required to interpret the drawings. This motivates our current solution called Digitize-PID which comprises of an end-to-end pipeline for detection of core components from P&IDs like pipes, symbols and textual information, followed by their association with each other and eventually, the validation and correction of output data based on inherent domain knowledge. A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented in the paper. In addition, we have created an annotated synthetic dataset, Dataset-P&ID, of 500 P&IDs by incorporating different types of noise and complex symbols which is made available for public use (currently there exists no public P&ID dataset). We evaluate our proposed method on this synthetic dataset and a real-world anonymized private dataset of 12 P&ID sheets. Results show that Digitize-PID outperforms the existing state-of-the-art for P&ID digitization.