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Proceedings Paper

Variational dynamic background model for keyword spotting in handwritten documents
Author(s): Gaurav Kumar; Safwan Wshah; Venu Govindaraju
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Paper Abstract

We propose a bayesian framework for keyword spotting in handwritten documents. This work is an extension to our previous work where we proposed dynamic background model, DBM for keyword spotting that takes into account the local character level scores and global word level scores to learn a logistic regression classifier to separate keywords from non-keywords. In this work, we add a bayesian layer on top of the DBM called the variational dynamic background model, VDBM. The logistic regression classifier uses the sigmoid function to separate keywords from non-keywords. The sigmoid function being neither convex nor concave, exact inference of VDBM becomes intractable. An expectation maximization step is proposed to do approximate inference. The advantage of VDBM over the DBM is multi-fold. Firstly, being bayesian, it prevents over-fitting of data. Secondly, it provides better modeling of data and an improved prediction of unseen data. VDBM is evaluated on the IAM dataset and the results prove that it outperforms our prior work and other state of the art line based word spotting system.

Paper Details

Date Published: 24 March 2014
PDF: 9 pages
Proc. SPIE 9021, Document Recognition and Retrieval XXI, 902104 (24 March 2014); doi: 10.1117/12.2041244
Show Author Affiliations
Gaurav Kumar, Univ. at Buffalo (United States)
Safwan Wshah, Univ. at Buffalo (United States)
Venu Govindaraju, Univ. at Buffalo (United States)


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

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