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Machine Learning in Document Analysis and Recognition

  • Book
  • © 2008

Overview

Editors:
  1. Simone Marinai

    1. Dipartimento di Sistemi e Informatica, University of Florence, Firenze, Italy

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  2. Hiromichi Fujisawa

    1. Hitachi Central Research Laboratory, Kokubunji-shi, Tokyo, Japan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

Part of the book series: Studies in Computational Intelligence (SCI, volume 90)

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Table of contents (16 chapters)

  1. Front Matter

    Pages I-XI
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  2. Introduction to Document Analysis and Recognition

    • Simone Marinai
    Pages 1-20

  3. Structure Extraction in Printed Documents Using Neural Approaches

    • Abdel Belaïd, Yves Rangoni
    Pages 21-43

  4. Machine Learning for Reading Order Detection in Document Image Understanding

    • Donato Malerba, Michelangelo Ceci, Margherita Berardi
    Pages 45-69

  5. Decision-Based Specification and Comparison of Table Recognition Algorithms

    • Richard Zanibbi, Dorothea Blostein, James R. Cordy
    Pages 71-103

  6. Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction

    • Floriana Esposito, Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro
    Pages 105-138

  7. Classification and Learning Methods for Character Recognition: Advances and Remaining Problems

    • Cheng-Lin Liu, Hiromichi Fujisawa
    Pages 139-161

  8. Combining Classifiers with Informational Confidence

    • Stefan Jaeger, Huanfeng Ma, David Doermann
    Pages 163-191

  9. Self-Organizing Maps for Clustering in Document Image Analysis

    • Simone Marinai, Emanuele Marino, Giovanni Soda
    Pages 193-219

  10. Adaptive and Interactive Approaches to Document Analysis

    • George Nagy, Sriharsha Veeramachaneni
    Pages 221-257

  11. Cursive Character Segmentation Using Neural Network Techniques

    • Michael Blumenstein
    Pages 259-275

  12. Multiple Hypotheses Document Analysis

    • Tatsuhiko Kagehiro, Hiromichi Fujisawa
    Pages 277-303

  13. Learning Matching Score Dependencies for Classifier Combination

    • Sergey Tulyakov, Venu Govindaraju
    Pages 305-332

  14. Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition

    • Tamás Varga, Horst Bunke
    Pages 333-360

  15. Review of Classifier Combination Methods

    • Sergey Tulyakov, Stefan Jaeger, Venu Govindaraju, David Doermann
    Pages 361-386

  16. Machine Learning for Signature Verification

    • Sargur N. Srihari, Harish Srinivasan, Siyuan Chen, Matthew J. Beal
    Pages 387-408

  17. Off-line Writer Identification and Verification Using Gaussian Mixture Models

    • Andreas Schlapbach, Horst Bunke
    Pages 409-428

  18. Back Matter

    Pages 429-433
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Keywords

About this book

The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With ?rst papers dating back to the 1960’s, DAR is a mature but still gr- ing research?eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this ?eld, while broader DAR techniques are nowadays studied and applied to other industrial and o?ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi?ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri?cation have also bene?ted much from machine learning algorithms.

Editors and Affiliations

  • Dipartimento di Sistemi e Informatica, University of Florence, Firenze, Italy

    Simone Marinai

  • Hitachi Central Research Laboratory, Kokubunji-shi, Tokyo, Japan

    Hiromichi Fujisawa

Bibliographic Information

  • Book Title: Machine Learning in Document Analysis and Recognition

  • Editors: Simone Marinai, Hiromichi Fujisawa

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-76280-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2008

  • Hardcover ISBN: 978-3-540-76279-9Published: 10 January 2008

  • Softcover ISBN: 978-3-642-09511-5Published: 23 November 2010

  • eBook ISBN: 978-3-540-76280-5Published: 27 December 2007

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 434

  • Number of Illustrations: 142 b/w illustrations

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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