Machine learning in document analysis and recognition / Simone Marinai, Hiromichi Fujisawa (eds).

Format:

Book

Contributor:

Marinai, Simone.

Fujisawa, Hiromichi.

Series:

Studies in computational intelligence

Language:

English

Subjects (All):

Machine learning.

Documentation--Data processing.

Documentation.

Optical pattern recognition.

Physical Description:

xi, 433 pages : illustrations ; 24 cm.

Place of Publication:

Berlin : Springer, 2008.

Summary:

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.

Contents:

Introduction to Document Analysis and Recognition / Simone Marinai 1

Structure Extraction in Printed Documents Using Neural Approaches / Abdel Belaid, Yves Rangoni 21

Machine Learning for Reading Order Detection in Document Image Understanding / Donato Malerba, Michelangelo Ceci, Margherita Berardi 45

Decision-Based Specification and Comparison of Table Recognition Algorithms / Richard Zanibbi, Dorothea Blostein, James R. Cordy 71

Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction / Floriana Esposito, Stefano Ferilli, Teresa M.A. Basile, Nicola Di Mauro 105

Classification and Learning Methods for Character Recognition: Advances and Remaining Problems / Cheng-Lin Liu, Hiromichi Fujisawa 139

Combining Classifiers with Informational Confidence / Stefan Jaeger, Huanfeng Ma, David Doermann 163

Self-Organizing Maps for Clustering in Document Image Analysis / Simone Marinai, Emanuele Marino, Giovanni Soda 193

Adaptive and Interactive Approaches to Document Analysis / George Nagy, Sriharsha Veeramachaneni 221

Cursive Character Segmentation Using Neural Network Techniques / Michael Blumenstein 259

Multiple Hypotheses Document Analysis / Tatsuhiko Kagehiro, Hiromichi Fujisawa 277

Learning Matching Score Dependencies for Classifier Combination / Sergey Tulyakov, Venu Govindaraju 305

Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition / Tamas Varga, Horst Bunke 333

Review of Classifier Combination Methods / Sergey Tulyakov, Stefan Jaeger, Venu Govindaraju, David Doermann 361

Machine Learning for Signature Verification / Sargur N. Srihari, Harish Srinivasan, Siyuan Chen, Matthew J. Beal 387

Off-line Writer Identification and Verification Using Gaussian Mixture Models / Andreas Schlapbach, Horst Bunke 409.

Notes:

Includes bibliographical references and index.

ISBN:

9783540762799

3540762795

OCLC:

191751854