Machine Learning P&ID Tag Extraction via Neural Networks
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Solution Overview
Problem
Current methods for extracting and categorizing tags in P&ID diagrams are inefficient and inaccurate, relying on manual efforts or OCR technology, which are neither cost-effective nor robust.
Innovation Solution
The implementation of a machine learning-based system that uses convolutional neural networks and other algorithms to identify and classify symbols, tags, and character patterns in P&ID diagrams, employing synthetic training data to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual efforts or OCR technology are used to extract and categorize tags in P&ID diagrams, then the process can be performed with simple technology, but the efficiency and accuracy are poor
Solution Approach 1:
The patent replaces manual mechanical inspection and OCR-based text recognition with a machine learning-based symbol recognition system. The system uses trained models to automatically identify and categorize symbols in P&ID diagrams, achieving both high efficiency and high accuracy simultaneously by substituting human labor and simple optical recognition with intelligent algorithms
Solution Approach 2:
The patent changes the operational parameters of the recognition system by using multiple trained machine learning models with different specialization (e.g., different symbol types, different diagram standards). By adjusting which model is applied based on the specific diagram characteristics, the system achieves high accuracy across diverse P&ID variations while maintaining efficient automated processing
2Productivity
If machine learning models are used to identify symbols and tags in P&ID diagrams, then accuracy and efficiency are improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex symbol recognition task into multiple specialized machine learning models, each trained to recognize specific types of symbols or diagram standards. This segmentation allows the system to handle complexity through modular architecture, where each model is relatively simple but the combination achieves comprehensive recognition capability
Solution Approach 2:
The patent introduces an intermediary layer of trained machine learning models that mediate between the input P&ID diagram and the final extracted tags. These models serve as intelligent intermediaries that automatically perform the complex recognition and categorization tasks, shielding the user from the underlying system complexity while delivering high productivity
Data Source
AI summary
Systems and methods for categorizing patterns of characters in a document by utilizing machine based learning techniques include generating character classification training data, building a character classification model based on the character classification training data; obtaining an image that includes a pattern of characters, the characters including one or more contours, applying the character classification model to the image to classify the contours, and applying the labels to clusters of the contours.