P&ID Data Extraction Using OCR and R-CNN
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Solution Overview
Problem
Existing techniques struggle to efficiently extract machine-readable information from piping and instrumentation drawings (P&IDs) in image-only formats, which are often of poor quality and require lengthy manual review or error-prone pattern recognition algorithms.
Innovation Solution
A method using machine learning algorithms, specifically an optical character recognition (OCR) algorithm and a Faster Region-based Convolutional Neural Network (R-CNN) architecture, to predict bounding boxes and labels for text boxes and symbols in P&IDs, while also inferring properties for detected symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recreation of P&IDs is used to extract machine-readable information, then information accuracy is improved, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of recreating P&IDs with an automated optical character recognition (OCR) system. The OCR algorithm automatically extracts text and symbols from image-only P&ID formats, converting them into machine-readable information without requiring human operators to manually recreate each element, thereby dramatically reducing time consumption while maintaining extraction accuracy.
Solution Approach 2:
The patent introduces an OCR algorithm as an intermediary between the image-only P&ID format and the machine-readable information. This intermediary automatically processes the visual data, extracting text boxes, symbols, and connections through automated recognition and classification, eliminating the need for direct manual intervention while preserving information fidelity.
2Extent of automation
If pattern recognition algorithms are used to extract information from P&IDs, then automation level is improved, but accuracy decreases due to image quality issues and variations
Solution Approach 1:
The patent changes the parameters of the pattern recognition approach by using OCR-based text extraction instead of traditional pixel-by-pixel image matching. This parameter change allows the system to handle variations in image quality, resolution, and visual artifacts more effectively, as OCR can recognize text despite these variations, thereby maintaining higher accuracy while preserving automation.
Solution Approach 2:
The patent substitutes traditional pattern recognition algorithms with an OCR-based system that is specifically designed to handle degraded image quality. The OCR technology uses linguistic knowledge and pattern matching at the character level rather than requiring exact pixel matches, making it robust against low resolution, visual artifacts, and obscured portions while maintaining high automation.
3Measurement precision
If custom image libraries are created for every P&ID to improve pattern recognition, then detection accuracy is improved, but device complexity and preparation time increase
Solution Approach 1:
The patent creates a universal OCR-based extraction system that can process all P&ID images without requiring custom libraries for each specific P&ID. The OCR algorithm is designed to recognize standard engineering symbols and text formats across different P&IDs, making the system universally applicable and eliminating the complexity of creating and maintaining separate image libraries for each drawing.
Solution Approach 2:
The patent extracts only the essential text and symbol information from P&IDs using OCR, rather than requiring complete custom image libraries. By focusing on extracting key machine-readable elements (text boxes, symbol labels, connections) through selective recognition, the system achieves sufficient accuracy without the overhead of comprehensive custom library creation and management.
Data Source
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AI summary
In example embodiments, techniques are provided for using machine learning to extract machine-readable labels for text boxes and symbols in P&IDs in image-only formats. A P&ID data extraction application uses an optical character recognition (OCR) algorithm to predict labels for text boxes in a P&ID. The P&ID data extraction application uses a first machine learning algorithm to detect symbols in the P&ID and return a predicted bounding box and predicted class of equipment for each symbol. One or more of the predicted bounding boxes may be decimate by non-maximum suppression to avoid overlapping detections. The P&ID data extraction application uses a second machine learning algorithm to infer properties for each detected symbol having a remaining predicted bounding box. The P&ID data extraction application stores the predicted bounding box and a label including the predicted class of equipment and inferred properties in a machine-readable format.