Markov Models for Handwriting Recognition

Nonfiction, Computers, Advanced Computing, Engineering, Optical Data Processing, Computer Vision, General Computing
Cover of the book Markov Models for Handwriting Recognition by Thomas Plötz, Gernot A. Fink, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Thomas Plötz, Gernot A. Fink ISBN: 9781447121886
Publisher: Springer London Publication: February 2, 2012
Imprint: Springer Language: English
Author: Thomas Plötz, Gernot A. Fink
ISBN: 9781447121886
Publisher: Springer London
Publication: February 2, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer London

Cover of the book Pathology of the Larynx by Thomas Plötz, Gernot A. Fink
Cover of the book Cardiac Electrophysiology by Thomas Plötz, Gernot A. Fink
Cover of the book Atlas of Pediatric Cardiac Surgery by Thomas Plötz, Gernot A. Fink
Cover of the book Finance for IT Decision Makers by Thomas Plötz, Gernot A. Fink
Cover of the book Cardiac CT Imaging by Thomas Plötz, Gernot A. Fink
Cover of the book Complications in Knee and Shoulder Surgery by Thomas Plötz, Gernot A. Fink
Cover of the book Contemporary Interventional Ultrasonography in Urology by Thomas Plötz, Gernot A. Fink
Cover of the book Vascular Surgery by Thomas Plötz, Gernot A. Fink
Cover of the book Logic Programming with Prolog by Thomas Plötz, Gernot A. Fink
Cover of the book Diagnosis and Management of Marfan Syndrome by Thomas Plötz, Gernot A. Fink
Cover of the book Bladder Cancer by Thomas Plötz, Gernot A. Fink
Cover of the book Dynamical Systems by Thomas Plötz, Gernot A. Fink
Cover of the book The Seductive Computer by Thomas Plötz, Gernot A. Fink
Cover of the book Clinical Cardiac Electrophysiology in Clinical Practice by Thomas Plötz, Gernot A. Fink
Cover of the book Diagnostic Imaging of the Kidney and Urinary Tract in Children by Thomas Plötz, Gernot A. Fink
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy