P&ID Diagram Extraction: OCR vs AI Symbol Recognition

Executive summary

DIGATEX was established by leading industry specialists to support organizations across the energy and resources sectors in their digital transformation journeys. Our objective is to unlock greater value from asset and engineering information by enabling assets to be designed, built, operated, and maintained more safely, productively, and efficiently. 

Through the application of artificial intelligence and machine learning, DIGATEX provides a cost-effective solution that delivers outcomes significantly faster than traditional manual methods. This enables rapid access to reliable engineering data, supporting informed decision-making based on accurate and readily available information.

DIGATEX achieves this by combining deep industry domain expertise with advanced data science and the practical application of artificial intelligence, including convolutional neural networks, machine learning, and enhanced optical character recognition. This approach redefines the way data is extracted from diverse information sources and transformed into digitally intelligent documents that can be efficiently searched, analyzed, validated, and integrated into enterprise workflows.

The DIGATEX platform supports tag extraction, symbol recognition, metadata extraction, document-to-asset linking, validation workflows, and contextual interpretation of complex engineering drawing collections. By doing this, rather than isolating extracted text, DIGATEX preserves the relationship between equipment, pipeline sections, valves, instrumentation, and other components.

P&ID extraction refers to the process of converting a piping and instrumentation diagram into formatted data. Instead of having to read every line of the drawing, pipeline, symbol, and tag manually, AI identifies all of the components, understands their function, and turns them into usable records. Such records may include process equipment, valves, instruments, control devices, connections, and flow paths.

Traditional OCR methods can read text such as tag numbers and labels and lines, but these methods cannot relate the symbols to one another. Modern AI bridges the gap by recognizing ID symbols, instrument symbols, and other graphic symbols; by tracing flow directions; interpreting control loops; and understanding how all of the components of an entire system work together. The outcome of this is structured data that engineers can use without having to review the same drawing time and again.

What Is P&ID Extraction?

A P&ID is more than just a simple technical drawing; it’s also the primary design reference for building, operating, maintaining, and modifying a facility. It depicts the process flow, process equipment, instruments, valves, control devices, pipeline routes, and many other components that are required for industrial operations.

At its core, P&ID (piping and instrumentation diagram) extraction converts information from a piping and instrumentation diagram into structured data. With a modern extraction workflow, both text and graphical information can be accurately captured through the combination of optical character recognition (OCR) and artificial intelligence (AI) vision models.

This is important for every area of the process industry, whether they are oil and gas facilities, pharmaceutical factories, plant processes, or control systems. Accurate extraction will enhance the safety of operations, improve maintenance practices, facilitate easier document management, and produce reliable engineering records. Instead of searching through static drawing files, the team uses electronic, search-friendly databases that preserve the original engineering intent and critical information.

OCR vs AI Symbol Recognition: What Is the Difference?

OCR and AI symbol recognition are both designed to solve different problems. OCR is designed to read text, whereas AI is designed to understand the engineering meaning behind the drawing.

Comparison Point	OCR	AI Symbol Recognition
Main function	Reads tags, labels, specifications, and loop number values	Recognizes ID symbols, instrument and device symbols, graphic symbols, shape, devices, line types, and connections
Best for	Text extraction from technical drawing files	Understanding flow directions, control loops, pipeline relationships, and equipment
What it can miss	Symbol meaning, signal types, physical sequence, and how components are connected	May require validation for poor-quality scans or unusual symbols
Engineering value	Creates searchable data	Builds structured P&ID extraction information for analysis, maintenance, operations, and safety
Example output	Equipment tags, labels, loop number, notes	Valve symbol, sensor, control device, pipeline connection, flow direction

OCR works well when reading written information contained on a P&ID drawing. However, it cannot understand the engineering context. For example, OCR may read the tag next to a valve correctly, but it cannot ascertain whether the tag belongs to the valve, a nearby sensor, or other piece of equipment. In addition, it has no way of explaining the function of symbols or how all the components interact.

AI symbol recognition does not only read text; it also recognizes shape, interprets indicating symbols, traces every pipeline, follows flow directions, and establishes how all the devices are connected throughout the system. Many of the newer software platforms also perform graph-based analysis to convert the visual schematic into a digital network that reflects the true engineering relationships instead of just creating the same diagram digitally.

A P&ID is a schematic that uses symbols to communicate the function, combination, and identification of engineering components rather than a scale model (actual size). It enables AI to reconstruct the logical relationships of the engineering systems even when their physical layout has not been provided.

What Data Can AI Extract from a P&ID Diagram?

A modern AI platform can do much more than simply extract isolated tags. The purpose of the extraction is to maintain relationships between equipment, instruments, valves, and every pipeline so that the data produced will be reflective of how the system functions. The structured output can be used by engineering software, asset registers, digital twins, and reporting tools without having to manually review the original instrumentation diagram.

Data Type	Example	Why It Matters
Process equipment	Pumps, vessels, heat exchangers	Builds equipment registers and supports asset management
Valves	Gate, globe, ball, control valves	Supports isolation planning and maintenance
Instruments	Pressure, temperature, level transmitters	Creates instrument indexes and improves traceability
Sensors	Temperature or pressure sensors	Supports monitoring and automation
Line information	Pipeline numbers and service	Helps identify routing and operating conditions
Control devices	Actuators and controllers	Documents automation logic
Control loops	Flow and pressure loops	Verifies automation design
Sampling lines	Laboratory sampling points	Supports quality control and compliance
Materials	Pipe class and construction materials	Assists procurement and engineering reviews
Specifications	Pressure ratings and design notes	Supports validation and installation
Connections	Equipment-to-pipeline relationships	Preserves engineering context

Why P&ID Diagrams Are Hard to Extract Manually

Due to the presence of hundreds or tens of thousands of engineering symbols, line crossings, annotations, and equipment tags that are contained in every instrumentation diagram, manual extraction is very difficult. Scanned documents used by older plants often include faded text, handwritten updates, or multiple layers of revisions on the same drawing.

Different industry standards can add another level of challenge. Symbol libraries differ substantially between companies and projects, and instrument symbols and ID symbols may have changed over decades of engineering work. Engineers also have to differentiate between flow, instrument signals, and utility services and also ensure every identification is correct.

Manual review of a process flow diagram is very exhausting and tedious because many important details appear very small in size. A missed valve, incorrect tag, or broken pipeline connection can affect engineering records and later operations.

These same issues are more important in gas facilities, control systems, and other industrial systems where documentation is critical for ensuring safe operations. For example, all drawings must accurately depict the specific mechanical equipment installed during shutdown schemes’ safety reviews. AI can be used to extract information automatically from P&ID drawings, and this will eliminate or significantly reduce manual work and the chance of transcription errors that may affect safety.

Use Cases for P&ID Diagram Extraction

Organizations are investing in P&ID extraction so that structured engineering data can be reused in multiple business functions. Teams can create searchable records instead of having to check each drawing separately for engineering, procurement, maintenance, and compliance functions.

The most common applications are the following:

• Equipment list creation: AI identifies pumps, vessels, heat exchangers, valves, and other equipment and helps to create structured asset registers, which the engineering department can review and update faster.
• Instrument index generation: The system can extract instruments, tags, loop number values, and identification details and generate accurate instrument indexes and reduce manual data input.
• Valve and line list extraction: AI automatically captures valves, pipeline details, line numbers, and specifications, enabling the creation of complete valves and line lists for both engineering and operations.
• Maintenance planning: Searchable data enables the maintenance team to locate the equipment that is affected and how it is connected, enabling them to plan their maintenance and inspections with greater confidence.
• Safety analysis: Structured data supports hazard reviews by allowing the reviewers to verify process paths, isolation points, control functions, etc. that affect safety.
• Control loop verification: AI can trace control loops, controllers, sensors, etc. to ensure that automation documentation is consistent with the intended system design.
• Digital handover: This allows project teams to provide validated engineering data for asset management and operational systems, rather than delivering thousands of static drawing files.
• Building a single source of truth: Compiling extracted information from every P&ID into one source for engineering records eliminates inconsistencies between diagrams, spreadsheets, and databases.
• Data preparation for engineering systems: Data will be structured so that it can be exported into CMMS, EAM, digital twin platforms, and other engineering applications without having to manually recreate information from every technical drawing.
• Automatic bill of materials support: AI can help generate a complete bill of materials (BOM) for procurement and project planning by identifying components, materials, equipment, and pipeline information.

Process Flow and Equipment Register Creation

From a process flow diagram and instrumentation diagram, AI can identify major equipment, heat exchangers, pumps, tanks, and compressors, valves, and pipeline sections and create structured asset registers that preserves identification, location, and engineering relationship.

This not only reduces manual work during project handover, installation, and future modifications projects, but also help engineers to compare extracted process flow data against current database to identify missing assets or outdated records. The ability of AI to identify other components connected throughout the process provides a much more detailed data compared to basic OCR.

Control Loops, Instruments, and Signal Types

AI recognition identifies every control loop and its associated loop number, controller, associated instruments, control devices, and sensors. It also recognizes the various signal types, follows their connections, and understands the function of each device in the overall automation architecture.

This allows for easier analysis during troubleshooting, and it verifies that each control loop matches engineering documentation. Increased visibility of the automation logic will improve maintenance, support safer operations, and reduce the time and effort needed to modify the system or validate the updated ID diagram.

How DIGATEX Supports P&ID Extraction

The key to success is the ability to accurately identify all tagged items, extract the tags and the associated data (e.g., failure mode for a valve, size, alarms), and provide context. This means identifying the object classifications (Gate Valve, Pipeline, Pressure Indicator Instrument, etc.) and linking data on the P&ID to other data sources (schedules and datasheets).

DIGATEX provides a human machine interface called Knowledge Hub, which is a web browser-driven agent that enables users to search for tagged items and view them, then mark up the documents, collaborate with other users, and create tag hierarchies.

DIGATEX has processed over 39,000 P&ID’s using their world-leading AI Platform and addressed a variety of different use cases, including:

• Extracting all tagged items and producing a consolidated tag register
• Identifying which valves are in which lines
• Linking Nozzles on equipment vessels to the metadata (nozzle type, spec, etc.)
• Generating tag hierarchies for maintenance system configuration
• Providing data to populate digital twins
• Providing data from unintelligent documents, such as PDF, TIF, JPEG, etc., for use in operations and commissioning
• Identifying what has changed from one revision to another
• Enabling collaboration between remote teams

Rather than treating P&ID extraction as a simple OCR, DIGATEX views it as an engineering data platform. DIGATEX combines OCR with AI-powered visual interpretation capabilities to transform information contained in technical drawings into structured, searchable, and ready for downstream engineering workflows.

The platform supports tag extraction, symbol recognition, metadata extraction, document-to-asset linking, validation workflows, and contextual interpretation of complex engineering drawing collections. By doing this, rather than isolating extracted text, DIGATEX preserves the relationship between equipment, pipeline sections, valves, instrumentation, and other components.

FAQs About P&ID Extraction

Can OCR Extract Data from P&ID Diagrams?

Yes, OCR can extract text information such as tags, labels, and loop number values from P&ID drawings. However, it does not understand how equipment and devices are linked nor does it understand symbols or flow.

Why Is AI Symbol Recognition Better for P&ID Extraction?

AI symbol recognition is better for P&ID extraction because it understands the engineering context behind an instrumentation diagram. It can identify pipeline path, valves, equipment, symbols, and control relationships. OCR can’t do this.

What Information Is Stored in a Piping and Instrumentation Diagram?

A piping and instrumentation diagram contains information about the equipment, piping, valves, instruments, flow, connections, control logic, materials, operational details, and engineering identifications used at a facility such as gas facilities.

How Does P&ID Extraction Improve Maintenance and Operations?

P&ID extraction improves maintenance and operations by converting your complex engineering drawings into searchable data that can help your team quickly locate equipment, verify valves, trace pipelines, perform analysis, support safety, and generate accurate engineering records.