AI Document Recognition for Rotated Text and P&ID Symbols
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Solution Overview
Problem
Industrial environments face challenges in converting mixed media documents, such as P&ID diagrams, into searchable digital formats due to limitations in text recognition software, which struggles with rotated text and combined text and image elements, and lacks effective recognition of specific symbols and images representing industrial hardware.
Innovation Solution
An Autonomous Intelligent Decision Support System and Server that uses AI to recognize and differentiate between text and images within industrial references, converting them into a searchable format by preprocessing, tiling, rotating, and reconstructing documents, and training AI to identify industrial images and symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text recognition software is used on scanned documents, then text can be extracted from clean backgrounds, but the software fails to recognize text when images and text are combined or text is rotated
Solution Approach 1:
The system segments the document processing into distinct stages: preprocessing to separate text regions from images, rotation detection and correction, and then text recognition. This segmentation allows each component to be optimized independently, solving the problem of recognizing rotated text and handling combined media elements
Solution Approach 2:
The system performs preliminary actions before text recognition by detecting and correcting rotations, separating text from images, and preprocessing the document structure. These preliminary steps prepare the document so that conventional text recognition software can then accurately recognize text that would otherwise be missed due to rotation or mixed media
2Ease of operation
If paper documents are scanned and converted to searchable PDF, then text can be searched, but the scope is limited to text against standard clean background and cannot recognize industrial symbols
Solution Approach 1:
The system introduces an intermediary image recognition layer between the scanned document and the search function. This intermediary recognizes industrial symbols, equipment, and images, extracts their metadata, and integrates it with the text search capability, enabling comprehensive search across both text and visual elements
Solution Approach 2:
The system creates a universal search platform that handles multiple document types (P&ID diagrams, electrical plans, process flows) and multiple content types (text, images, symbols, diagrams) through a single interface, making the search function adaptable to diverse industrial reference materials
3Ease of manufacture
If multiple personnel manually convert documents to digital format, then conversion can be completed, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service automated conversion where scanned documents are automatically processed through preprocessing, text recognition, image recognition, symbol identification, and searchable format generation without requiring manual intervention. This autonomous processing dramatically reduces both time and labor requirements while maintaining high conversion quality
Data Source
AI summary
A system that includes artificial intelligence (AI) configured to identify text and images within an industrial reference. Example industrial references include electrical drawings and P&IDs. The system includes a method for training artificial intelligence model to recognize text characters and strings in addition to industrial images using a limited sample set. The use of a limited sample set improves computer performance by relying on a smaller dataset to train the model.