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© 2011

Markov Models for Handwriting Recognition

  • Introduces the typical architecture of a Markov model-based handwriting recognition system

  • Describes the essential theoretical concepts behind Markovian models

  • Provides a thorough review of the solutions proposed in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition

Book

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Thomas Plötz, Gernot A. Fink
    Pages 1-8
  3. Thomas Plötz, Gernot A. Fink
    Pages 9-17
  4. Thomas Plötz, Gernot A. Fink
    Pages 19-26
  5. Thomas Plötz, Gernot A. Fink
    Pages 27-45
  6. Thomas Plötz, Gernot A. Fink
    Pages 47-66
  7. Thomas Plötz, Gernot A. Fink
    Pages 67-75

About this book

Introduction

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.

Keywords

Document Analysis Handwriting Recognition Hidden Markov Models Machine Learning Offline Handwriting Recognition Online Handwriting Recognition Pattern Recognition Reading Systems n-Gram Language Models

Authors and affiliations

  1. 1.Culture Lab, School of Computing ScienceNewcastle UniversityNewcastle upon TyneUnited Kingdom
  2. 2.Department of Computer ScienceTechnische Universität DortmundDortmundGermany

Bibliographic information

  • Book Title Markov Models for Handwriting Recognition
  • Authors Thomas Plötz
    Gernot A. Fink
  • Series Title SpringerBriefs in Computer Science
  • DOI https://doi.org/10.1007/978-1-4471-2188-6
  • Copyright Information Thomas Plötz 2011
  • Publisher Name Springer, London
  • eBook Packages Computer Science Computer Science (R0)
  • Softcover ISBN 978-1-4471-2187-9
  • eBook ISBN 978-1-4471-2188-6
  • Series ISSN 2191-5768
  • Series E-ISSN 2191-5776
  • Edition Number 1
  • Number of Pages VI, 78
  • Number of Illustrations 5 b/w illustrations, 0 illustrations in colour
  • Topics Pattern Recognition
  • Buy this book on publisher's site
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Reviews

From the reviews:

“The book provides a general introduction with 75 pages for researchers on handwriting recognition. More contents focus on the handwriting recognition methods based on Markov models, including a recognition framework and techniques within this framework. … this book gives an introduction for researchers on handwriting recognition. I think readers can get some useful information from it.” (Longlong Ma, IAPR Newsletter, Vol. 35 (2), April, 2013)