A Statistical and Structural Approach for Symbol Recognition, Using XML Modelling

  • Mathieu Delalandre
  • Pierre Héroux
  • Sébastien Adam
  • Eric Trupin
  • Jean-Marc Ogier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

This paper deals with the problem of symbol recognition in technical document interpretation. We present a system using a statistical and structural approach. This system uses two interpretation levels. In a first level, the system extracts and recognizes the loops of symbols. In the second level, it relies on proximity relations between the loops in order to rebuild loop graphs, and then to recognize the complete symbols. Our aim is to build a generic device, so we have tried to outsource models descriptions and tools parameters. Data manipulated by our system are modelling in XML. This gives the system the ability to interface tools using different communication data structures, and to create graphic representation of process results.

Keywords

Zernike Moment Distance Constraint Statistical Recognition Scalable Vector Graphic Symbol Recognition 
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 2002

Authors and Affiliations

  • Mathieu Delalandre
    • 1
  • Pierre Héroux
    • 1
  • Sébastien Adam
    • 1
  • Eric Trupin
    • 1
  • Jean-Marc Ogier
    • 2
  1. 1.Laboratory PSIUniversity of RouenMont Saint AignanFrance
  2. 2.Laboratory L3IUniversity of La RochelleLa RochelleFrance

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