Intelligent Indexing: A Semi-Automated, Trainable System for Field Labeling ID: 2014-022
A cutting-edge system designed to revolutionize the indexing of historical documents by blending human expertise with machine learning for improved accuracy and efficiency.

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Technology Overview
Developed by Robert Clawson and William Barrett at Brigham Young University, Intelligent Indexing is a novel system that facilitates the labeling of fields within documents, such as the 1920 Utah census, by integrating human intelligence with machine learning. This system is specifically designed to enhance both the efficiency and accuracy of document field labeling through a user-friendly interface and advanced learning mechanisms.
Key Advantages
- Minimized context switching through a direct interaction user interface, featuring color coding and auto-complete
- Advanced preprocessing of document images to isolate fields for efficient comparison and labeling
- Adaptive learning mechanism that improves labeling accuracy based on user interactions
- Transitive learning capability, allowing the labeling of one field to facilitate the automatic labeling of similar fields, thereby boosting efficiency
Problems Addressed
- Reduces the time required for indexing historical documents significantly
- Improves the accuracy of field labeling within documents
- Enhances user experience in document indexing tasks by reducing context switching and simplifying interactions
Market Applications
- Archival and library sciences, particularly in the digitization and indexing of historical documents
- Government and public record keeping, for efficient organization and retrieval of census data and other public records
- Academic research, facilitating easier access to accurately indexed historical documents for scholarly work
Additional Information
Technology ID: 2014-022
Sell Sheet: Download the Sell Sheet here
Market Analysis: Contact us for a more in-depth market report
Date Published: 06 May, 2025
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