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Modeling, Learning, and Processing of Text Technological Data Structures pp 221–247Cite as

Machine Learning for Document Structure Recognition

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Part of the Studies in Computational Intelligence book series (SCI,volume 370)

Abstract

The backbone of the information age is digital information which may be searched, accessed, and transferred instantaneously. Therefore the digitization of paper documents is extremely interesting. This chapter describes approaches for document structure recognition detecting the hierarchy of physical components in images of documents, such as pages, paragraphs, and figures, and transforms this into a hierarchy of logical components, such as titles, authors, and sections. This structural information improves readability and is useful for indexing and retrieving information contained in documents. First we present a rule-based system segmenting the document image and estimating the logical role of these zones. It is extensively used for processing newspaper collections showing world-class performance. In the second part we introduce several machine learning approaches exploring large numbers of interrelated features. They can be adapted to geometrical models of the document structure, which may be set up as a linear sequence or a general graph. These advanced models require far more computational resources but show a better performance than simpler alternatives and might be used in future.

Keywords

  • Document Image
  • Text Line
  • Conditional Random Field
  • Parse Tree
  • Text Region

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-22613-7_12
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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Schloss Birlinghoven, D-53754, Sankt Augustin, Germany

    Gerhard Paaß & Iuliu Konya

Authors
  1. Gerhard Paaß
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  2. Iuliu Konya
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Editor information

Editors and Affiliations

  1. Faculty of Linguistics and Literature, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany

    Alexander Mehler

  2. Institute of Cognitive Science, University of Osnabrück, Albrechtstr. 28, 49076, Osnabrück, Germany

    Kai-Uwe Kühnberger

  3. Angewandte Sprachwissenschaft und, Justus-Liebig-Universität Gießen, Computerlinguistik, Otto-Behaghel-Straße 10D, 35394, Gießen, Germany

    Henning Lobin & Harald Lüngen & 

  4. Institut für deutsche Sprache und Literatur, Technical University Dortmund, Emil-Figge-Straße 50, 44227, Dortmund, Germany

    Angelika Storrer

  5. SFB 441 Linguistic Data Structures, Eberhard Karls Universität Tübingen, Nauklerstraße 35, 72074, Tübingen, Germany

    Andreas Witt

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Paaß, G., Konya, I. (2011). Machine Learning for Document Structure Recognition. In: Mehler, A., Kühnberger, KU., Lobin, H., Lüngen, H., Storrer, A., Witt, A. (eds) Modeling, Learning, and Processing of Text Technological Data Structures. Studies in Computational Intelligence, vol 370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22613-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-22613-7_12

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