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Brazilian Symposium on Document Image Analysis

BSDIA 1997: Advances in Document Image Analysis pp 60–73Cite as

Objective evaluation of the discriminant power of features in an HMM-based word recognition system

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1339))

Abstract

This paper describes an elegant method for evaluating the discriminant power of features in the framework of an HMM-based word recognition system. This method employs statistical indicators, entropy and perplexity, to quantify the capability of each feature to discriminate between classes without resorting to the result of the recognition phase. The HMMs and the Viterbi algorithm are used as powerful tools to automatically deduce the probabilities required to compute the above mentioned quantities.

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Authors and Affiliations

  1. Centre for Pattern Recognition and Machine Intelligence Department of Computer Science, Concordia University, 1455 de Maisonneuve Boulevard West, H3G 1MB, Montréal, Canada

    A. El-Yacoubi, R. Sabourin & C. Y. Suen

  2. Service de Recherche Technique de La Poste Department Reconnaissance, Modélisation Optimisation (RMO), 10, rue de l'ile Mabon, 44063, Nantes Cedex 02, France

    A. El-Yacoubi & M. Gilloux

  3. Ecole de Technologie Supérieure, Laboratoire d'imagerie, de vision et d'intelligence artificielle (LIVIA), 1100, Notre-Dame Ouest, Canada

    R. Sabourin

Authors
  1. A. El-Yacoubi
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  2. M. Gilloux
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  3. R. Sabourin
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  4. C. Y. Suen
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Editor information

Nabeel A. Murshed Flávio Bortolozzi

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© 1997 Springer-Verlag Berlin Heidelberg

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El-Yacoubi, A., Gilloux, M., Sabourin, R., Suen, C.Y. (1997). Objective evaluation of the discriminant power of features in an HMM-based word recognition system. In: Murshed, N.A., Bortolozzi, F. (eds) Advances in Document Image Analysis. BSDIA 1997. Lecture Notes in Computer Science, vol 1339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63791-5_4

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  • DOI: https://doi.org/10.1007/3-540-63791-5_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63791-2

  • Online ISBN: 978-3-540-69646-9

  • eBook Packages: Springer Book Archive

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