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Proceedings Paper

Two-stage approach to keyword spotting in handwritten documents
Author(s): Mehdi Haji; Mohammad R. Ameri; Tien D. Bui; Ching Y. Suen; Dominique Ponson
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Paper Abstract

Separation of keywords from non-keywords is the main problem in keyword spotting systems which has traditionally been approached by simplistic methods, such as thresholding of recognition scores. In this paper, we analyze this problem from a machine learning perspective, and we study several standard machine learning algorithms specifically in the context of non-keyword rejection. We propose a two-stage approach to keyword spotting and provide a theoretical analysis of the performance of the system which gives insights on how to design the classifier in order to maximize the overall performance in terms of F-measure.

Paper Details

Date Published: 24 March 2014
PDF: 12 pages
Proc. SPIE 9021, Document Recognition and Retrieval XXI, 90210P (24 March 2014); doi: 10.1117/12.2042265
Show Author Affiliations
Mehdi Haji, IMDS Software (Canada)
Concordia Univ. (Canada)
Mohammad R. Ameri, Concordia Univ. (Canada)
Tien D. Bui, Concordia Univ. (Canada)
Ching Y. Suen, Concordia Univ. (Canada)
Dominique Ponson, IMDS Software (Canada)

Published in SPIE Proceedings Vol. 9021:
Document Recognition and Retrieval XXI
Bertrand Coüasnon; Eric K. Ringger, Editor(s)

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