Overview
- 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
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (6 chapters)
Keywords
About this book
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains 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, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden Markov models and 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 reviews proposed solutions in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.
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)
Authors and Affiliations
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
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Thomas Plötz 2011
Softcover ISBN: 978-1-4471-2187-9Published: 10 September 2011
eBook ISBN: 978-1-4471-2188-6Published: 02 February 2012
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: VI, 78
Number of Illustrations: 5 b/w illustrations
Topics: Pattern Recognition