ML Diagram Label Recognition for Unstructured Images
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Solution Overview
Problem
Existing automated systems fail to accurately associate labels with diagram components in unstructured images, particularly in hand-drawn diagrams, due to ambiguity in shape and edge correspondences, leading to time-consuming and error-prone manual processes.
Innovation Solution
A machine learning-based approach employing object detection and text block relation scoring to identify correct shapes and edges corresponding to labels, using deep learning models and OCR services to decode textual content and determine reading orders within bounding boxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated label recognition is implemented using simple proximity-based association, then automation extent is improved, but measurement precision deteriorates due to ambiguity in hand-drawn diagrams
Solution Approach 1:
The patent segments the label association problem into multiple independent analysis dimensions: spatial proximity analysis, textual content analysis, graphical structure analysis, and contextual relationship analysis. Each dimension processes specific features independently and their results are combined to make the final association decision, thereby maintaining high automation while improving precision.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw diagram features and label associations. This model integrates multiple feature types (spatial, textual, graphical) and uses learned relationships to determine associations, resolving the ambiguity that simple proximity-based methods cannot handle.
2Measurement precision
If manual label association is performed to ensure accuracy, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The system enables self-service automated label recognition by having the machine learning model independently analyze diagram features, generate associations, and produce results without human intervention. The model serves itself by learning from training data and automatically applying learned patterns to new diagrams, achieving both high accuracy and efficiency.
Solution Approach 2:
The patent performs preliminary extraction and analysis of spatial, textual, and graphical features before the actual label association decision. By pre-processing and organizing features in advance, the system prepares comprehensive input for the machine learning model, enabling fast and accurate association without time-consuming manual processing.
3Device complexity
If simple proximity-based label association is used, then device complexity is reduced, but reliability deteriorates due to improper entity association
Solution Approach 1:
The patent segments the association method into distinct modular components: feature extraction module, machine learning model module, and association decision module. Each component handles specific tasks independently, making the overall system manageable despite its complexity while ensuring reliable associations through systematic processing.
Solution Approach 2:
The machine learning model serves as an intermediary that bridges simple feature extraction and reliable association decisions. It processes multiple feature types and learns complex relationships between labels and diagram entities, ensuring reliable associations without requiring the entire system to be overly complex.
4Measurement precision
If multiple feature types are analyzed for label association, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the multi-feature analysis system into independent feature extraction modules (spatial features, textual features, graphical features) that process different types of information separately. This modular segmentation allows the system to analyze multiple feature types comprehensively while keeping each component relatively simple and manageable.
Data Source
AI summary
Mechanisms are disclosed for machine learning-based diagram label recognition in connection with diagrams represented by unstructured images. An unstructured image of the diagram is received. A plurality of text blocks associated with the diagram is detected. The text blocks are associated with labels contained within the diagram. Textual contents of the text blocks and the labels are recognized. The labels are associated with corresponding edges and shapes within the diagram.