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Hidden Markov Models in Handwriting Recognition

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Fundamentals in Handwriting Recognition

Part of the book series: NATO ASI Series ((NATO ASI F,volume 124))

Abstract

Hidden Markov Models (HMM) have now became the prevalent paradigm in automatic speech recognition. Only recently, several researchers in off-line handwriting recognition have tried to transpose the HMM technology to their field after realizing that word images could be assimilated to sequences of observations. HMM’s form a family of tools for modelling sequential processes in a statistical and generative manner. Their reputation is due to the results attained in speech recognition which derive mostly from the existence of automatic training techniques and the advantages of the probabilistic framework. This article first reviews the basic concepts of HMM’s. The second part is devoted to illustrative applications in the field of off- line handwriting recognition. We describe four different applications of HMM’s in various contexts and review some of the other approaches.

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Author information

Authors and Affiliations

  1. La Poste, Service de Recherche Technique de la Poste, SRTP/RD/RVA, 10, Rue de l’Île Mabon, F-44038, Nantes Cedex, France

    Michel Gilloux

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  1. Michel Gilloux
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, Via Amendola 173, I-70126, Bari, Italy

    Sebastiano Impedovo

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© 1994 Springer-Verlag Berlin Heidelberg

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Gilloux, M. (1994). Hidden Markov Models in Handwriting Recognition. In: Impedovo, S. (eds) Fundamentals in Handwriting Recognition. NATO ASI Series, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-78646-4_15

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  • DOI: https://doi.org/10.1007/978-3-642-78646-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-78648-8

  • Online ISBN: 978-3-642-78646-4

  • eBook Packages: Springer Book Archive

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