Recognition of degraded handwritten digits using dynamic Bayesian networks

Laurence Likforman-Sulem; Marc Sigelle

doi:10.1117/12.702791

29 January 2007 Recognition of degraded handwritten digits using dynamic Bayesian networks

Proceedings Volume 6500, Document Recognition and Retrieval XIV; 65000G (2007) https://doi.org/10.1117/12.702791
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Laurence Likforman-Sulem and Marc Sigelle "Recognition of degraded handwritten digits using dynamic Bayesian networks", Proc. SPIE 6500, Document Recognition and Retrieval XIV, 65000G (29 January 2007); https://doi.org/10.1117/12.702791

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