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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  1. 1.
    Plamondon, R., Srihari, S.N.: On-Line and off-line handwritten recognition: A comprehensive survey. IEEE Trans on PAMI 22, 62–84 (2000)Google Scholar
  2. 2.
    Pal, U., Chaudhuri, B.B.: Indian script character recognition: A Survey. Pattern Recognition 37, 1887–1899 (2004)CrossRefGoogle Scholar
  3. 3.
    Chaudhuri, B.B., Pal, U.: A complete printed Bangla OCR system. Pattern Recognition 31, 531–549 (1998)CrossRefGoogle Scholar
  4. 4.
    Aparna, K.G., Ramakrishnan, A.G.: A Complete Tamil Optical Character Recognition System. In: Proc. in the 5th Intl workshop on Document Analysis and Systems, pp. 53–57 (2002)Google Scholar
  5. 5.
    Bansal, V., Sinha, R.M.K.: Integrating Knowledge Sources in Devanagari Text Recognition. IEEE Transaction on Systems, Man and Cybernetics 30, 4 (2000)Google Scholar
  6. 6.
    Yu, D., Yan, H.: Reconstruction of broken handwritten digits based on structural morphological features. Pattern Recognition 34(2), 235–254 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Cai, J., Liu, Z.Q.: Integration of structural and statistical information for unconstrained handwritten character recognition. IEEE PAMI 21, 263–270 (1999)Google Scholar
  8. 8.
    Byan, H., Lee, S.W.: A Survey on pattern recognition application of support vector machines. IJPRAI 17, 459–486 (2003)Google Scholar
  9. 9.
    Wunsch, P., Laine, A.F.: Wavelet Descriptors for Multi-resolution Recognition of Hand-printed Digits. Pattern Recognition 28, 56–66 (1995)CrossRefGoogle Scholar
  10. 10.
    Chin, Z., Yan, H.: A handwritten character recognition using self-organizing maps and fuzzy rules. Pattern Recognition 22, 923–937 (2000)Google Scholar
  11. 11.
    Kim, K., Bang, S.Y.: A handwritten character classification using tolerant Rough set. IEEE Trans. on PAMI 22, 923–937 (2000)Google Scholar
  12. 12.
    Wakabayashi, T., Tsuruoka, S., Kimura, F., Miyake, Y.: “Increasing the Feature size in handwritten Numeral Recognition to improve accuracy. System and Computers in Japan 26(8), 35–44 (1995)CrossRefGoogle Scholar
  13. 13.
    Hanmandlu, M., Ramana Murthy, O.V.: Fuzzy Model Based Recognition of Handwritten Hindi Numerals. In: Intl.Conf. on Cognition and Recognition, pp. 490–496 (2005)Google Scholar
  14. 14.
    Ramteke, R.J., Borkar, P.D., Mehrotra, S.C.: Recognition of Marathi Handwritten Numerals: An Invariant Moments Approach. In: Intl.Conf. on Cognition and Recognition, pp. 482–489 (2005)Google Scholar
  15. 15.
    Bajaj, R., Dey, L., Chaudhury, S.: Devnagari numeral recognition by combining decision of multiple connectionist classifiers. Sadhana, Part. 1 27, 59–72 (2002)CrossRefGoogle Scholar
  16. 16.
    Kumar, S., Singh, C.: A Study of Zernike Moments and its use in Devnagari Handwritten Character Recognition. In: Intl.Conf. on Cognition and Recognition, pp. 514–520 (2005)Google Scholar
  17. 17.
    Sethi, I.K., Chatterjee, B.: Machine Recognition of constrained Hand printed Devnagari. Pattern Recognition 9, 69–75 (1977)CrossRefGoogle Scholar
  18. 18.
    Bhattacharya, U., Chaudhuri, B.B., Ghosh, R., Ghosh, M.: On Recognition of Handwritten Devnagari Numerals. In: Proc. of the Workshop on Learning Algorithms for Pattern Recognition (in conjunction with the 18th Australian Joint Conference on Artificial Intelligence), Sydney, pp. 1–7 (2005)Google Scholar
  19. 19.
    Roy, K., Pal, U., Kimura, F.: Recognition of Handwritten Bangla Characters. In: Proc. 2nd International Conference on Machine Intelligence (ICMI), pp. 480–485 (2005)Google Scholar
  20. 20.
    Bhattacharya, U., Chaudhuri, B.B.: Databases for research on recognition of handwritten characters of Indian scripts. In: Proc. 8th ICDAR, pp. 789–793 (2005)Google Scholar
  21. 21.
    Pal, U., Roy, K., Kimura, F.: A Lexicon Driven Method for Unconstrained Bangla Handwritten Word Recognition. In: 10th International Workshop on Frontiers in Handwriting Recognition ((accepted, 2006)Google Scholar

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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