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Proceedings Paper

Recognition of degraded handwritten digits using dynamic Bayesian networks
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Paper Abstract

We investigate in this paper the application of dynamic Bayesian networks (DBNs) to the recognition of handwritten digits. The main idea is to couple two separate HMMs into various architectures. First, a vertical HMM and a horizontal HMM are built observing the evolving streams of image columns and image rows respectively. Then, two coupled architectures are proposed to model interactions between these two streams and to capture the 2D nature of character images. Experiments performed on the MNIST handwritten digit database show that coupled architectures yield better recognition performances than non-coupled ones. Additional experiments conducted on artificially degraded (broken) characters demonstrate that coupled architectures better cope with such degradation than non coupled ones and than discriminative methods such as SVMs.

Paper Details

Date Published: 29 January 2007
PDF: 8 pages
Proc. SPIE 6500, Document Recognition and Retrieval XIV, 65000G (29 January 2007); doi: 10.1117/12.702791
Show Author Affiliations
Laurence Likforman-Sulem, Ecole Nationale Supérieure des Télécommunications, TSI (France)
CNRS LTCI (France)
Marc Sigelle, Ecole Nationale Supérieure des Télécommunications, TSI (France)
CNRS LTCI (France)

Published in SPIE Proceedings Vol. 6500:
Document Recognition and Retrieval XIV
Xiaofan Lin; Berrin A. Yanikoglu, Editor(s)

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