175 years of Springer publishing +++ Through June 30: 50% off Physics & Astronomy Books

Studies in Computational Intelligence

Machine Learning in Document Analysis and Recognition

Editors: Marinai, Simone, Fujisawa, Hiromichi (Eds.)

  • Presents applications and learning algorithms for Document Image Analysis and Recognition (DIAR)
  • Identifies good practices for the use of learning strategies in DIAR
see more benefits

Buy this book

eBook $189.00
price for USA (gross)
  • ISBN 978-3-540-76280-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $249.00
price for USA
  • ISBN 978-3-540-76279-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $249.00
price for USA
  • ISBN 978-3-642-09511-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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.

Table of contents (16 chapters)

  • Introduction to Document Analysis and Recognition

    Marinai, Simone

    Pages 1-20

  • Structure Extraction in Printed Documents Using Neural Approaches

    Belaïd, Abdel (et al.)

    Pages 21-43

  • Machine Learning for Reading Order Detection in Document Image Understanding

    Malerba, Donato (et al.)

    Pages 45-69

  • Decision-Based Specification and Comparison of Table Recognition Algorithms

    Zanibbi, Richard (et al.)

    Pages 71-103

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

    Esposito, Floriana (et al.)

    Pages 105-138

Buy this book

eBook $189.00
price for USA (gross)
  • ISBN 978-3-540-76280-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $249.00
price for USA
  • ISBN 978-3-540-76279-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $249.00
price for USA
  • ISBN 978-3-642-09511-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Document Analysis and Recognition
Editors
  • Simone Marinai
  • Hiromichi Fujisawa
Series Title
Studies in Computational Intelligence
Series Volume
90
Copyright
2008
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-76280-5
DOI
10.1007/978-3-540-76280-5
Hardcover ISBN
978-3-540-76279-9
Softcover ISBN
978-3-642-09511-5
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XII, 434
Number of Illustrations and Tables
142 b/w illustrations
Topics