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Proceedings Paper

Intelligent indexing: a semi-automated, trainable system for field labeling
Author(s): Robert Clawson; William Barrett
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Paper Abstract

We present Intelligent Indexing: a general, scalable, collaborative approach to indexing and transcription of non-machinereadable 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 video demonstration is available at http://youtube.com/gqdVzEPnBEw)

Paper Details

Date Published: 14 January 2015
PDF: 12 pages
Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020A (14 January 2015); doi: 10.1117/12.2076862
Show Author Affiliations
Robert Clawson, Brigham Young Univ. (United States)
William Barrett, Brigham Young Univ. (United States)


Published in SPIE Proceedings Vol. 9402:
Document Recognition and Retrieval XXII
Eric K. Ringger; Bart Lamiroy, Editor(s)

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