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Proceedings Paper

Segmentation-free keyword spotting framework using dynamic background model
Author(s): Gaurav Kumar; Safwan Wshah; Venu Govindaraju; Sitaram Ramachandrula
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Paper Abstract

We propose a segmentation free word spotting framework using Dynamic Background Model. The proposed approach is an extension to our previous work where dynamic background model was introduced and integrated with a segmentation based recognizer for keyword spotting. The dynamic background model uses the local character matching scores and global word level hypotheses scores to separate keywords from non-keywords. We integrate and evaluate this model on Hidden Markov Model (HMM) based segmentation free recognizer which works at line level without any need for word segmentation. We outperform the state of the art line level word spotting system on IAM dataset.

Paper Details

Date Published: 4 February 2013
PDF: 8 pages
Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580H (4 February 2013); doi: 10.1117/12.2008597
Show Author Affiliations
Gaurav Kumar, Univ. at Buffalo, SUNY (United States)
Safwan Wshah, Univ. at Buffalo, SUNY (United States)
Venu Govindaraju, Univ. at Buffalo, SUNY (United States)
Sitaram Ramachandrula, Hewlett-Packard Labs. (India)

Published in SPIE Proceedings Vol. 8658:
Document Recognition and Retrieval XX
Richard Zanibbi; Bertrand Coüasnon, Editor(s)

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