Feature Approach for Printed Document Image Analysis

  • Jean Duong
  • Myrian Côté
  • Hubert Emptoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


This paper presents advances in zone classification for printed document image analysis. It firstly introduces entropic heuristic for text separation problem. Then a brief recall on existing texture and geometric discriminant parameters proposed in a previous research is done. Several of them are chosen and modified to perform statistical pattern recognition. For each of these two aspects, experiments are done. A document image database with groundtruth is used. Available results are discussed.


Support Vector Machine Linear Discriminant Analysis Document Image Radial Basis Function Kernel Horizontal Projection 
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

  • Jean Duong
    • 1
    • 2
  • Myrian Côté
    • 1
  • Hubert Emptoz
    • 2
  1. 1.Ecole de Technologie SupérieureLaboratoire d’Imagerie Vision et Intelligence Artificielle (LIVIA)MontréalCanada
  2. 2.Institut National des Sciences Appliquées (INSA) de LyonLaboratoire de Reconnaissance de Formes et Vision (RFV)Villeurbanne CEDEX

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