Machine Learning in Document Analysis and Recognition

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160,49 

Studies in Computational Intelligence 90

ISBN: 3642095119
ISBN 13: 9783642095115
Herausgeber: Simone Marinai/Hiromichi Fujisawa
Verlag: Springer Verlag GmbH
Umfang: xii, 434 S., 142 s/w Illustr., 434 p. 142 illus.
Erscheinungsdatum: 23.11.2010
Auflage: 1/2008
Produktform: Kartoniert
Einband: KT

Presents applications and learning algorithms for Document Image Analysis and Recognition (DIAR)Identifies good practices for the use of learning strategies in DIARIncludes supplementary material: sn.pub/extras

Artikelnummer: 1048914 Kategorie:

Beschreibung

InhaltsangabeIntroduction to Document Analysis and Recognition.- Structure Extraction in Printed Documents Using Neural Approaches.- Machine Learning for Reading Order Detection in Document Image Understanding.- Decision-Based Specification and Comparison of Table Recognition Algorithms.- Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction.- Classification and Learning Methods for Character Recognition: Advances and Remaining Problems.- Combining Classifiers with Informational Confidence.- Self-Organizing Maps for Clustering in Document Image Analysis.- Adaptive and Interactive Approaches to Document Analysis.- Cursive character segmentation using neural network Techniques.- Multiple Hypotheses Document Analysis.- Learning Matching Score Dependencies for Classifier Combination.- Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition.- Review of Classifier Combination Methods.- Machine Learning for Signature Verification.- Off-line Writer Identification and Verification Using Gaussian Mixture Models.

Inhaltsverzeichnis

Introduction to Document Analysis and Recognition.- Structure Extraction in Printed Documents Using Neural Approaches.- Machine Learning for Reading Order Detection in Document Image Understanding.- Decision-Based Specification and Comparison of Table Recognition Algorithms.- Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction.- Classification and Learning Methods for Character Recognition: Advances and Remaining Problems.- Combining Classifiers with Informational Confidence.- Self-Organizing Maps for Clustering in Document Image Analysis.- Adaptive and Interactive Approaches to Document Analysis.- Cursive character segmentation using neural network Techniques.- Multiple Hypotheses Document Analysis.- Learning Matching Score Dependencies for Classifier Combination.- Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition.- Review of Classifier Combination Methods.- Machine Learning for Signature Verification.- Off-line Writer Identification and Verification Using Gaussian Mixture Models.

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