Markov Models for Handwriting Recognition

Markov Models for Handwriting Recognition PDF

Author: Thomas Plötz

Publisher: Springer Science & Business Media

Published: 2012-02-02

Total Pages: 82

ISBN-13: 1447121880

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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.

Markov Models for Pattern Recognition

Markov Models for Pattern Recognition PDF

Author: Gernot A. Fink

Publisher: Springer Science & Business Media

Published: 2014-01-14

Total Pages: 275

ISBN-13: 1447163087

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This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.

Hidden Markov Models: Applications In Computer Vision

Hidden Markov Models: Applications In Computer Vision PDF

Author: Horst Bunke

Publisher: World Scientific

Published: 2001-06-04

Total Pages: 246

ISBN-13: 9814491470

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Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).

Fundamentals in Handwriting Recognition

Fundamentals in Handwriting Recognition PDF

Author: Sebastiano Impedovo

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 499

ISBN-13: 3642786464

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For many years researchers in the field of Handwriting Recognition were considered to be working in an area of minor importance in Pattern Recog nition. They had only the possibility to present the results of their research at general conferences such as the ICPR or publish their papers in journals such as some of the IEEE series or PR, together with many other papers generally oriented to the more promising areas of Pattern Recognition. The series of International Workshops on Frontiers in Handwriting Recog nition and International Conferences on Document Analysis and Recognition together with some special issues of several journals are now fulfilling the expectations of many researchers who have been attracted to this area and are involving many academic institutions and industrial companies. But in order to facilitate the introduction of young researchers into the field and give them both theoretically and practically powerful tools, it is now time that some high level teaching schools in handwriting recognition be held, also in order to unite the foundations of the field. Therefore it was my pleasure to organize the NATO Advanced Study Institute on Fundamentals in Handwriting Recognition that had its origin in many exchanges among the most important specialists in the field, during the International Workshops on Frontiers in Handwriting Recognition.

Handwriting Recognition Using Neural Networks and Hidden Markov Models

Handwriting Recognition Using Neural Networks and Hidden Markov Models PDF

Author: Markus E. Schenkel

Publisher:

Published: 1995

Total Pages: 148

ISBN-13: 9783891918777

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"This work presents a writer independent system for on-line handwriting recognition which processes cursive script and handprint in a variety of writing styles. It uses a combination of artificial neural netsorks and hidden Markov models. Its main features are: word level recognition, training from examples, recognition based segmentation and integration of contextual information"--Page 4 of cover.

Knowledge-Based Intelligent Techniques in Character Recognition

Knowledge-Based Intelligent Techniques in Character Recognition PDF

Author: Lakhmi C. Jain

Publisher: CRC Press

Published: 2020-12-18

Total Pages: 316

ISBN-13: 1000151875

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Knowledge-Based Intelligent Techniques in Character Recognition presents research results on intelligent character recognition techniques, reflecting the tremendous worldwide interest in the applications of knowledge-based techniques in this challenging field. This resource will interest anyone involved in computer science, computer engineering, applied mathematics, or related fields. It will also be of use to researchers, application engineers and students who wish to develop successful character recognition systems such as those used in reading addresses in a postal routing system or processing bank checks. Features