A Shape Representation Scheme for Hand-Drawn Symbol Recognition

  • Pulabaigari Viswanath
  • T. Gokaramaiah
  • Gouripeddi V. Prabhakar Rao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)


Pen based inputs are natural for human beings. A hand-drawn shape (symbol) can be used for various purposes, like, a command gesture, an input for authentication purpose, etc. Shape of a symbol is invariant to scale, translation, mirror-reflection and rotation of the symbol. Moments, like Zernike moments are often used to represent a symbol. Descriptors based on Zernike moments are rotation invariant, but since they are neither translation nor scale invariant, a normalization step as pre-processing is required. Apart from this, higher order Zernike moments are error prone. The present paper, proposes to use probability distributions of some local moments of lower order, as a representation scheme. Theoretically it is shown to possess all invariance properties. Experimentally, using the k-nearest neighbor classifier (with Kullback-Leibler distance), it is shown to perform better than Zernike moments based representation scheme.


handwritten symbol recognition moments moment invariants probability distribution nearest neighbor classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Artieres, T., Marukatat, S., Gallinari, P.: Online handwritten shape recognition using segmental hidden Markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 205–217 (2007)CrossRefGoogle Scholar
  2. 2.
    Chong, C.W., Raveendran, P., Mukundan, P.: A comparitive analysis of algorithms for fast computation of Zernike moments. Pattern Recognition 36, 731–742 (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. A Wiley-interscience Publication, John Wiley & Sons (2000)Google Scholar
  4. 4.
    Flusser, J.: On the independence of rotation moment invariants. Pattern Recognition 33(9), 1405–1410 (2000)CrossRefGoogle Scholar
  5. 5.
    Flusser, J.: Moment invariants in image analysis. Proceedings of World Academy of Science, Engineering and Technology 11, 196–201 (2006)Google Scholar
  6. 6.
    Flusser, J., Suk, T., Zitova, B.: Moments and Moment Invariants in Pattern Recognition. John Wiley & Sons, UK (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Frankish, C., Hull, R., Morgan, P.: Recognition accuracy and user acceptance of pen. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp. 503–510 (1995)Google Scholar
  8. 8.
    Gokaramaiah, T., Viswanath, P., Reddy, B.: A novel shape based hierarchical retrieval system for 2D images. In: 2010 IEEE International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom), pp. 10–14 (October 2010)Google Scholar
  9. 9.
    Gui, J., Zhou, W.: Fruit shape classification using zernike moments. In: Proceedings of International Conference on Image Processing and Pattern Recognition in Industrial Engineering, vol. 7820 (August 2010)Google Scholar
  10. 10.
    Hse, H., Newton, A.R.: Sketched symbol recognition using Zernike moments. In: Proceedings of ICPR 2004, vol. 1, pp. 367–370. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  11. 11.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1967)zbMATHGoogle Scholar
  12. 12.
    Kenzie, I.M., Zhang, S.: The immediate usability of graffiti. In: Proc. Graphics Interface, pp. 129–137 (1997)Google Scholar
  13. 13.
    Lio, S.X., Pawlak, M.: On the accuracy of zernike moments for image analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1358–1364 (1996)CrossRefGoogle Scholar
  14. 14.
    Pal, U., Chaudhuri, B.B.: Indian script character recognition: a survey. Pattern Recognition 37(9), 1887–1899 (2004)CrossRefGoogle Scholar
  15. 15.
    Reiss, T.: The revised fundamental theorem of moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 830–834 (1991)CrossRefGoogle Scholar
  16. 16.
    Teague, M.R.: Image analysis via the general theory of moments. Journal of Optical Society of America 70(8), 920–930 (1980)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rohatgi, V.K., Ehsanes Saleh, A.K.: An Introduction to Probability and Statistics. A Wiley-Interscience Publication, John Wiley & Sons, Inc. (2002)Google Scholar
  18. 18.
    Wang, Z., Chi, Z., Feng, D.: Shape based leaf image retrieval. IEE Proc.-Vis. Image Signal Process. 150 (February 2003)Google Scholar
  19. 19.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pulabaigari Viswanath
    • 1
  • T. Gokaramaiah
    • 1
  • Gouripeddi V. Prabhakar Rao
    • 1
  1. 1.Departments of CSE and ITRajeev Gandhi Memorial College of Engineering & TechnologyNandyalIndia

Personalised recommendations