Polar Transformation System for Offline Handwritten Character Recognition

  • Xianjing Wang
  • Atul Sajjanhar
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 368)


Offline handwritten recognition is an important automated process in pattern recognition and computer vision field. This paper presents an approach of polar coordinate-based handwritten recognition system involving Support Vector Machines (SVM) classification methodology to achieve high recognition performance. We provide comparison and evaluation for zoning feature extraction methods applied in Polar system. The recognition results we proposed were trained and tested by using SVM with a set of 650 handwritten character images. All the input images are segmented (isolated) handwritten characters. Compared with Cartesian based handwritten recognition system, the recognition rate is more stable and improved up to 86.63%.


Support Vector Machine Feature Extraction Recognition Rate Character Recognition Feature Extraction Method 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, Z., Cai, J., Buse, R.: Handwriting recognition: soft computing and probabilistic approaches. Springer, Berlin (2003)MATHGoogle Scholar
  2. 2.
    Deodhare, D., Suri, N.R., Amit, S.R.: Preprocessing and image enhancement algorithms for a form-based intelligent character recognition system. International Journal of Computer Science & Applications 2, 131–144 (2005)Google Scholar
  3. 3.
    Chandhuri, B.B.: Digital document processing: major direction and recent advances. Springer, London (2007)Google Scholar
  4. 4.
    Yan, C., Leedham, G.: Decompose-threshold approach to handwriting extraction in degraded historical document images, pp. 239–244. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  5. 5.
    Nishida, H.: An approach to integration of off-line and on-line recognition of handwriting, pp. 1213–1219. Elsevier Science Inc., Amsterdam (1995)Google Scholar
  6. 6.
    Melin, P., Castillo, O., Ramirez, E.G., Janusz, K., Pedrycz, W.: Analysis and design of intelligent systems using soft conputing techniques. Springer, Berlin (2007)CrossRefGoogle Scholar
  7. 7.
    Trier, D.O., Jain, K.A., Taxt, T.: Feature extraction methods for character recognition. Pattern Recognition 29, 641–662 (1996)CrossRefGoogle Scholar
  8. 8.
    Pechwitz, M., Margner, V.: Baseline estimation for Arabic handwriting words. In: Proceedings Frontiers in Handwriting Recognition, pp. 479–484 (2002)Google Scholar
  9. 9.
    Simone, A.B.K., Cinthia, F.O.A.: Perceptual zoning for handwritten character recognition. In: Proceedings of 12th Conference of IGS, pp. 178–182 (2005)Google Scholar
  10. 10.
    Rajashekararadhya, S.V., Ranjan, P.V.: Efficient Zone Based Feature Extraction Algorithm for Handwritten Numeral Recognition of Rour Popular South Indian Script. Journal of Theoretical and Applied Information Technology, 1171–1181 (2005)Google Scholar
  11. 11.
    Majumdar, A., Chaudhuri, B.B.: Printed and handwritten bangla numeral recognition using multiple classifier outputs. In: Proceedings of the First IEEE ICSIP 2006, vol. 1, pp. 190–195 (2006)Google Scholar
  12. 12.
    Hanmandlu, M., Grover, J., Madasu, V.K.: Inout fuzzy for the recognition of handwritten Hindi numerals. In: International Conference on Informational Technology, pp. 208–213 (2007)Google Scholar
  13. 13.
    Vapnik, V.N.: The nature of statistical learning theory. Wiley, New York (1995)MATHGoogle Scholar
  14. 14.
    Muller, K.R., Mika, S., Ratsh, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)CrossRefGoogle Scholar
  15. 15.
    Camastra, F.: A SVM-based cursive character recognizer. Pattern Recognition Society 40, 3721–3727 (2007)MATHCrossRefGoogle Scholar
  16. 16.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification. Department of Computer Science. National Taiwan University (2003)Google Scholar
  17. 17.
    Kim, H., Pang, S., Je, H., Kim, D., Bang, S.Y.: Constructing support vector machine ensemble. Pattern Recognition 36, 2757–2767 (2003)MATHCrossRefGoogle Scholar
  18. 18.
  19. 19.
    Nopsuwan chai, R.: Discriminative training method and their applications to handwriting recognition. Technical Report 652, University of Cambridge (2005)Google Scholar
  20. 20.
    Mpiperis, I., Malassiotis, S., Strintzis, M.G.: 3-D face recognition with the geodesic polar representation. IEEE Transaction on Information Forensics and Security, 537–547 (2007)Google Scholar
  21. 21.
    Blumenstein, M., Verma, B., Basli, H.: A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters. In: The 7th International Conference on Decument Analysis and Recognition (ICDAR 2003), pp. 137–141 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xianjing Wang
    • 1
  • Atul Sajjanhar
    • 1
  1. 1.School of Information TechnologyDeakin UniversityBurwoodAustralia

Personalised recommendations