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Symbolic Learning Techniques in Paper Document Processing

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Machine Learning and Data Mining in Pattern Recognition (MLDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1715))

Abstract

WISDOM++ is an intelligent document processing system that transforms a paper document into HTML/XML format. The main design requirement is adaptivity, which is realized through the application of machine learning methods. This paper illustrates the application of symbolic learning algorithms to the first three steps of document processing, namely document analysis, document classification and document understanding. Machine learning issues related to the application are: Efficient incremental induction of decision trees from numeric data, handling of both numeric and symbolic data in first-order rule learning, learning mutually dependent concepts. Experimental results obtained on a set of real-world documents are illustrated and commented.

Acknowledgments

The authors would like to thank Francesco De Tommaso, Dario Gerbino, Ignazio Sardella, Giacomo Sidella, Rosa Maria Spadavecchia, and Silvana Spagnoletta for their contribution to the development of WISDOM++. Thanks also to the authors of the systems C4.5 and ITI.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari, via Orabona 4, 70126, Bari, Italy

    Oronzo Altamura, Floriana Esposito, Francesca A. Lisi & Donato Malerba

Authors
  1. Oronzo Altamura
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  2. Floriana Esposito
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  3. Francesca A. Lisi
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  4. Donato Malerba
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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Arno-Nitzsche-Str. 45, D-04277, Leipzig, Germany

    Petra Perner

  2. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guilford, GU2 5XH, UK

    Maria Petrou

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© 1999 Springer-Verlag Berlin Heidelberg

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Altamura, O., Esposito, F., Lisi, F.A., Malerba, D. (1999). Symbolic Learning Techniques in Paper Document Processing. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_13

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  • DOI: https://doi.org/10.1007/3-540-48097-8_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66599-1

  • Online ISBN: 978-3-540-48097-6

  • eBook Packages: Springer Book Archive

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