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Markov Models for Handwriting Recognition [electronic resource] /by Thomas Plötz, Gernot A. Fink.

by Plötz, Thomas [author.]; Fink, Gernot A [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: London : Springer London, 2011.Description: VI, 78p. 5 illus. online resource.ISBN: 9781447121886.Subject(s): Computer science | Optical pattern recognition | Computer Science | Pattern RecognitionDDC classification: 006.4 Online resources: Click here to access online
Contents:
Introduction -- General Architecture -- Markov Model Concepts: The Essence -- Markov Model Based Handwriting Recognition -- Recognition Systems for Practical Applications -- Discussion.
In: Springer eBooksSummary: Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. Markov Models for Handwriting Recognition provides a comprehensive overview of the application of Markov models in the research field of handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text gives a thorough review of the solutions proposed in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.
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Introduction -- General Architecture -- Markov Model Concepts: The Essence -- Markov Model Based Handwriting Recognition -- Recognition Systems for Practical Applications -- Discussion.

Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. Markov Models for Handwriting Recognition provides a comprehensive overview of the application of Markov models in the research field of handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text gives a thorough review of the solutions proposed in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.

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