Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier

  • N. Sharma
  • U. Pal
  • F. Kimura
  • S. Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Recognition of handwritten characters is a challenging task because of the variability involved in the writing styles of different individuals. In this paper we propose a quadratic classifier based scheme for the recognition of off-line Devnagari handwritten characters. The features used in the classifier are obtained from the directional chain code information of the contour points of the characters. The bounding box of a character is segmented into blocks and the chain code histogram is computed in each of the blocks. Based on the chain code histogram, here we have used 64 dimensional features for recognition. These chain code features are fed to the quadratic classifier for recognition. From the proposed scheme we obtained 98.86% and 80.36% recognition accuracy on Devnagari numerals and characters, respectively. We used five-fold cross-validation technique for result computation.


Character Recognition Zernike Moment Contour Point Chain Code Handwritten Character 
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 2006

Authors and Affiliations

  • N. Sharma
    • 1
  • U. Pal
    • 1
  • F. Kimura
    • 2
  • S. Pal
    • 1
  1. 1.Computer Vision and Pattern Recognition UnitIndian Statistical InstituteKolkataIndia
  2. 2.Graduate School of EngineeringMie UniversityMieJapan

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