18079213. MACHINE LEARNING-BASED DIAGRAM LABEL RECOGNITION simplified abstract (SAP SE)
Contents
- 1 MACHINE LEARNING-BASED DIAGRAM LABEL RECOGNITION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING-BASED DIAGRAM LABEL RECOGNITION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Machine Learning-Based Diagram Label Recognition
- 1.13 Original Abstract Submitted
MACHINE LEARNING-BASED DIAGRAM LABEL RECOGNITION
Organization Name
Inventor(s)
Bernhard Schaefer of Worms (DE)
Andreas Gerber of Königsbach (DE)
MACHINE LEARNING-BASED DIAGRAM LABEL RECOGNITION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18079213 titled 'MACHINE LEARNING-BASED DIAGRAM LABEL RECOGNITION
Simplified Explanation
The patent application describes mechanisms for recognizing labels in diagrams represented by unstructured images using machine learning. Text blocks associated with the diagram are detected, and the textual contents of these blocks and labels are recognized.
- The patent application focuses on machine learning-based diagram label recognition in unstructured images.
- It involves detecting text blocks associated with labels in diagrams and recognizing their textual contents.
- Labels are then associated with corresponding edges and shapes within the diagram.
Key Features and Innovation
- Machine learning-based approach for diagram label recognition.
- Detection of text blocks associated with labels in unstructured images.
- Recognition of textual contents of text blocks and labels.
- Association of labels with corresponding edges and shapes within the diagram.
Potential Applications
This technology can be applied in various fields such as:
- Image recognition systems
- Document processing software
- Diagram editing tools
Problems Solved
- Automates the process of diagram label recognition in unstructured images.
- Improves accuracy and efficiency in identifying labels within diagrams.
- Reduces manual effort required for labeling diagrams.
Benefits
- Saves time and resources in labeling diagrams.
- Enhances the accuracy of label recognition in unstructured images.
- Streamlines the process of working with diagrams in digital formats.
Commercial Applications
Machine Learning-Based Diagram Label Recognition Technology for Image Processing Applications This technology can be utilized in image processing applications such as:
- Automated document analysis systems
- Graphic design software
- Educational tools for visual learning
Prior Art
Prior research in the field of image recognition and machine learning algorithms for text detection in images can provide valuable insights into related technologies.
Frequently Updated Research
Research on advancements in machine learning algorithms for image recognition and text detection in unstructured images can provide further enhancements to this technology.
Questions about Machine Learning-Based Diagram Label Recognition
How does this technology improve the efficiency of labeling diagrams in unstructured images?
This technology automates the process of detecting and recognizing labels within diagrams, reducing manual effort and improving accuracy.
What are the potential applications of this technology beyond diagram labeling?
This technology can be applied in various fields such as document processing, image recognition, and graphic design for enhanced visual content creation.
Original Abstract Submitted
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.
