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A Hidden Markov Models combination framework for handwriting recognition

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Abstract.

We propose a general framework to combine multiple sequence classifiers working on different sequence representations of a given input. This framework, based on Multi-Stream Hidden Markov Models (MS-HMMs), allows the combination of multiple HMMs operating on partially asynchronous information streams. This combination may operate at different levels of modeling: from the feature level to the post-processing level. This framework is applied to on-line handwriting word recognition by combining temporal and spatial representation of the signal. Different combination schemes are compared experimentally on isolated character recognition and word recognition tasks, using the UNIPEN international database.

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Authors and Affiliations

  1. LIP6, Université Paris 6, 8 rue du Capitaine Scott, 75015, France

    T. Artières, N. Gauthier & P. Gallinari

  2. EPH, Institut National des Télécommunications, 9 rue Charles Fourier, 91011, Evry, France

    B. Dorizzi

Authors
  1. T. Artières
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  2. N. Gauthier
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  3. P. Gallinari
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  4. B. Dorizzi
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Corresponding author

Correspondence to T. Artières.

Additional information

Received: 16 August 2002, Accepted: 21 November 2002, Published online: 6 June 2003

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Artières, T., Gauthier, N., Gallinari, P. et al. A Hidden Markov Models combination framework for handwriting recognition. IJDAR 5, 233–243 (2003). https://doi.org/10.1007/s10032-002-0096-2

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  • DOI: https://doi.org/10.1007/s10032-002-0096-2

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