We present Intelligent Indexing: a general, scalable, collaborative approach to indexing and transcription of non-machine-readable documents that exploits visual consensus and group labeling while harnessing human recognition and domain expertise. In our system, indexers work directly on the page, and with minimal context switching can navigate the page, enter labels, and interact with the recognition engine. Interaction with the recognition engine occurs through preview windows that allow the indexer to quickly verify and correct recommendations. This interaction is far superior to conventional, tedious, inefficient post-correction and editing. Intelligent Indexing is a trainable system that improves over time and can provide benefit even without prior knowledge. A user study was performed to compare Intelligent Indexing to a basic, manual indexing system. Volunteers report that using Intelligent Indexing is less mentally fatiguing and more enjoyable than the manual indexing system. Their results also show that it reduces significantly (30.2%) the time required to index census records, while maintaining comparable accuracy. A helpful video resource for learning more about this research is available on youtube through this link: https://www.youtube.com/watch?v=gqdVzEPnBEw
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Clawson, Robert T., "Intelligent Indexing: A Semi-Automated, Trainable System for Field Labeling" (2014). All Theses and Dissertations. 5307.
historical document processing, handwriting recognition, indexing, machine learning, human-computer interaction