Engineering Document Digitalization via OCR and Symbol Recognition
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Solution Overview
Problem
Current methods for digitalizing engineering documents, particularly Piping & Instrumentation Diagrams (P&IDs), struggle to accurately identify and connect graphical symbols, leading to incomplete digital models and limited reengineering capabilities, especially in process industries like chemical and oil & gas, where connectivity diagrams are crucial.
Innovation Solution
A method that uses Optical Character Recognition (OCR) and Optical Symbol Recognition (OSR) algorithms to identify non-parametric and parametric symbols, along with text annotations, to generate a digital model of plant topology, enabling the classification of connections and creation of high-level digital models suitable for reengineering tasks, without requiring extensive expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If OCR and OSR algorithms are used to identify symbols and text annotations, then the automation extent and productivity are improved, but the measurement precision and reliability of symbol identification deteriorate due to the complexity of engineering document graphics
Solution Approach 1:
The patent segments the engineering document analysis into distinct phases: text annotation extraction, symbol identification, and connectivity determination. Each phase uses specialized algorithms appropriate to its task, improving overall precision while maintaining automation. Text annotations are extracted first using OCR, then symbol identification uses pattern matching, and finally connectivity is determined through spatial analysis.
Solution Approach 2:
The patent introduces text annotations as intermediary elements that bridge the gap between automated recognition and accurate symbol identification. By first extracting text annotations that describe symbols, the system creates a reference framework that guides subsequent symbol identification, thereby improving accuracy without reducing automation.
2Ease of operation
If basic OSR capabilities are used for symbol recognition, then the ease of operation is improved, but the loss of information occurs because connectivity and structural digital models cannot be derived
Solution Approach 1:
The patent performs preliminary extraction of text annotations before symbol identification. This preliminary action captures descriptive information about symbols and their relationships, ensuring that connectivity and structural information are preserved in the digital model while keeping the overall process simple and automated.
Solution Approach 2:
The system uses extracted text annotations as feedback to guide symbol identification and connectivity determination. The annotations provide contextual information that validates and refines the automated recognition results, ensuring that connectivity information is accurately captured without complicating the operational process.
3Manufacturing precision
If comprehensive symbol identification and connectivity determination are implemented, then the manufacturing precision of digital models is improved, but the device complexity increases due to multiple processing steps
Solution Approach 1:
The patent divides the complex digitalization process into modular segments: text annotation extraction, symbol identification, and connectivity determination. Each module performs a specific function with dedicated algorithms, improving digital model precision while managing system complexity through clear separation of concerns and reusable components.
4Measurement precision
If manual digitalization services are used, then the measurement precision of symbol identification is improved, but the loss of time and productivity deteriorate due to extensive expertise requirements
Solution Approach 1:
The patent implements self-service capabilities where the system automatically extracts text annotations and uses them to guide symbol identification and connectivity determination. This reduces dependence on manual expert intervention while maintaining high accuracy, significantly reducing the time required for digitalization of engineering documents.
Data Source
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AI summary
The present disclosure relates to a method for digitalizing engineering documentation based on optical recognition and semantic analysis. The underlying notion of this approach is that certain graphical engineering documents, such as design diagrams, can be converted into object-oriented 'OO' models through the systematic recognition of text and symbolic forms, the detection of their connections, and the analysis of their domain-specific connotation. This invention introduces a method to capture the topology, connectivity, and annotations of design drawings typically stored on paper or elementary digital formats. Furthermore, it describes a method for the consistent representation of captured information on an OO model. Such a workflow and its underlying concepts are capable of drastically speeding-up the digitalization of legacy documents.