Combining Contour Based Orientation and Curvature Features for Writer Recognition

  • Imran Siddiqi
  • Nicole Vincent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)


This paper presents an effective method for writer recognition in handwritten documents. We have introduced a set of features that are extracted from two different representations of the contours of handwritten images. These features mainly capture the orientation and curvature information at different levels of observation, first from the chain code sequence of the contours and then from a set of polygons approximating these contours. Two writings are then compared by computing the distances between their respective features. The system trained and tested on a data set of 650 writers exhibited promising results on writer identification and verification.


Writer Recognition Freeman Chain Code Polygonization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Imran Siddiqi
    • 1
  • Nicole Vincent
    • 1
  1. 1.Laboratoire CRIP5 –SIPParis Descartes UniversityFrance

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