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Machine Learning in Document Analysis and Recognition [electronic resource] /edited by Simone Marinai, Hiromichi Fujisawa.

by Marinai, Simone [editor.]; Fujisawa, Hiromichi [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 90Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: XI, 433 p. 142 illus. online resource.ISBN: 9783540762805.Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world including pointers to challenges and opportunities for future research directions. The main goals of the book are identification of good practices for the use of learning strategies in DAR, identification of DAR tasks more appropriate for these techniques, and highlighting new learning algorithms that may be successfully applied to DAR.
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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.

The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world including pointers to challenges and opportunities for future research directions. The main goals of the book are identification of good practices for the use of learning strategies in DAR, identification of DAR tasks more appropriate for these techniques, and highlighting new learning algorithms that may be successfully applied to DAR.

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