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AWS Solutions Library

Guidance for Piping and Instrumentation Diagrams Digitization on AWS

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Overview

This Guidance demonstrates how to transform static Piping and Instrumentation Diagrams (P&IDs) into machine-readable digital formats using AWS AI and machine learning services. The process combines Amazon Bedrock Data Automation for superior text extraction from technical drawings with custom SageMaker deep learning models specifically designed to detect and classify industry-specific P&ID symbols and connections. After processing, the system generates structured JSON/DEXPI files containing precise symbol coordinates and relationships, alongside visual representations that highlight all detected elements. You can dramatically reduce manual digitization efforts while achieving higher accuracy in converting complex engineering diagrams into actionable digital assets that integrate with modern industrial systems.

Benefits

    Convert paper and digital P&ID drawings into structured engineering data with automated processing workflows. You can reduce manual digitization efforts while improving accuracy through specialized machine learning models designed for technical engineering notation.

    Enable seamless sharing of standardized engineering data across design, manufacturing, systems engineering, and quality teams. The solution produces machine-readable formats that integrate with existing systems, breaking down information silos between departments.

    Automate the extraction of symbols, text, and connections from complex technical diagrams using AWS's AI capabilities. Focus on engineering decisions rather than manual data entry while maintaining the integrity of critical industrial documentation.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

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