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Machine Learning for Document Structure Recognition

  • Gerhard Paaß
  • Iuliu Konya
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gerhard Paaß
    • 1
  • Iuliu Konya
    • 1
  1. 1.Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)Sankt AugustinGermany

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