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  • Book
  • © 2008

Machine Learning in Document Analysis and Recognition

  • 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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eBook USD 149.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-76280-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 199.99
Price excludes VAT (USA)
Hardcover Book USD 249.99
Price excludes VAT (USA)

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Table of contents (16 chapters)

  1. Front Matter

    Pages I-XI
  2. Machine Learning for Reading Order Detection in Document Image Understanding

    • Donato Malerba, Michelangelo Ceci, Margherita Berardi
    Pages 45-69
  3. Decision-Based Specification and Comparison of Table Recognition Algorithms

    • Richard Zanibbi, Dorothea Blostein, James R. Cordy
    Pages 71-103
  4. 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
  5. Combining Classifiers with Informational Confidence

    • Stefan Jaeger, Huanfeng Ma, David Doermann
    Pages 163-191
  6. Self-Organizing Maps for Clustering in Document Image Analysis

    • Simone Marinai, Emanuele Marino, Giovanni Soda
    Pages 193-219
  7. Adaptive and Interactive Approaches to Document Analysis

    • George Nagy, Sriharsha Veeramachaneni
    Pages 221-257
  8. Multiple Hypotheses Document Analysis

    • Tatsuhiko Kagehiro, Hiromichi Fujisawa
    Pages 277-303
  9. Learning Matching Score Dependencies for Classifier Combination

    • Sergey Tulyakov, Venu Govindaraju
    Pages 305-332
  10. Review of Classifier Combination Methods

    • Sergey Tulyakov, Stefan Jaeger, Venu Govindaraju, David Doermann
    Pages 361-386
  11. Machine Learning for Signature Verification

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

    Pages 429-433

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.

Keywords

  • Document Image Analysis and Recognition (DIAR)
  • Learning Strategies
  • algorithm
  • algorithms
  • calculus
  • classification
  • cognition
  • handwriting recognition
  • image analysis
  • layout
  • learning
  • machine learning
  • neural networks
  • self-organizing map
  • verification

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

Buying options

eBook USD 149.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-76280-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 199.99
Price excludes VAT (USA)
Hardcover Book USD 249.99
Price excludes VAT (USA)