Integration of Contextual Information in Online Handwriting Representation

  • Sara Izadi
  • Ching Y. Suen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


Robust handwriting recognition of complex patterns of arbitrary scale, orientation and location is yet elusive to date as reaching a good recognition rate is not trivial for most of the application developments in this field. Cursive scripts with complex character shapes, such as Arabic and Persian, make the recognition task even more challenging. This complexity requires sophisticated representations and learning methods, and comprehensive data samples. A direct approaches to achieve a better performance is focusing on designing more powerful building blocks of a handwriting recognition system which are pattern representation and pattern classification. In this paper we aim to scale up the efficiency of online recognition systems for Arabic characters by integrating novel representation techniques into efficient classification methods. We investigate the idea of incorporating two novel feature representations for online character data. We advocate the usefulness and practicality of these features in classification methods using neural networks and support vector machines. The combinations of proposed representations with related classifiers can offer a module for recognition tasks which can deal with any two-dimensional online pattern. Our empirical results confirm the higher distinctiveness and robustness to character deformations obtained by the proposed representation compared to currently available techniques.


  1. 1.
    Baghshah, M.S., Shouraki, S.B., Kasaei, S.: A novel fuzzy classifier using fuzzy LVQ to recognize online persian handwriting. In: ICTTA 2006: The 2nd Information and Communication Technologies From Theory to Applications, April 2006, vol. 1, pp. 1878–1883 (2006)Google Scholar
  2. 2.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 24(4), 509–522 (2002)CrossRefGoogle Scholar
  3. 3.
    Dinesh, M., Sridhar, M.K.: A feature based on encoding the relative position of a point in the character for online handwritten character recognition. In: ICDAR 2007: Proceedings of the 9th International Conference on Document Analysis and Recognition, pp. 1014–1017 (2007)Google Scholar
  4. 4.
    Halavati, R., Shouraki, S.B.: Recognition of Persian online handwriting using elastic fuzzy pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) 21(3), 491–513 (2007)CrossRefGoogle Scholar
  5. 5.
    Han, S., Chang, M., Zou, Y., Chen, X., Zhang, D.: Systematic multi-path HMM topology design for online handwriting recognition of east asian characters. In: ICDAR 2007: Proceedings of the 9th International Conference on Document Analysis and Recognition, pp. 604–608 (2007)Google Scholar
  6. 6.
    Hu, J., Rosenthal, A.S., Brown, M.K.: Combining high-level features with sequential local features for on-line handwriting recognition. In: ICIAP 1997: Proceedings of the 9th International Conference on Image Analysis and Processing, London, UK, vol. 2, pp. 647–654. Springer, Heidelberg (1997)Google Scholar
  7. 7.
    Huang, B.Q., Kechadi, M.-T.: A fast feature selection model for online handwriting symbol recognition. In: ICMLA ’06: Proceedings of the 5th International Conference on Machine Learning and Applications, Washington, DC, USA, pp. 251–257. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  8. 8.
    Izadi, S., Suen, C.Y.: Incorporating a new relational feature in Arabic online handwritten character recognition. In: VISAPP ’08: Proceedings of the Third International Conference on Computer Vision Theory and Applications, PortugalMadeira, Portugal, January 2008, vol. 1, pp. 559–562. INSTICC - Institute for Systems and Technologies of Information,Control and Communication (2008)Google Scholar
  9. 9.
    Klassen, T.J., Heywood, M.I.: Towards the on-line recognition of arabic characters. In: IJCNN 2002: Proceedings of the 2002 International Joint Conference on Neural Networks, May 2002, vol. 2, pp. 1900–1905 (2002)Google Scholar
  10. 10.
    Kressel, U.H.G.: Pairwise classification and support vector machines. In: Advances in kernel methods: support vector learning, pp. 255–268 (1999)Google Scholar
  11. 11.
    Liwicki, M.M., Bunke, H.: Feature selection for on-line handwriting recognition of whiteboard notes. In: IGS 2007: The 13th Conference of the International Graphonomics Society, pp. 101–105 (2007)Google Scholar
  12. 12.
    Mezghani, N., Mitiche, A., Cheriet, M.: A new representation of shape and its use for high performance in online Arabic character recognition by an associative memory. International Journal on Document Analysis and Recognition (IJDAR) 7(4), 201–210 (2005)CrossRefGoogle Scholar
  13. 13.
    Mezghani, N., Mitiche, A., Cheriet, M.: Bayes classification of online arabic characters by gibbs modeling of class conditional densities. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 30(7), 1121–1131 (2008)CrossRefGoogle Scholar
  14. 14.
    Randa, S.A.M., Elanwar, I., Rashwan, M.A.: Simultaneous segmentation and recognition of Arabic characters in an unconstrained on-line cursive handwritten document. International Journal of Computer and Information Science and Engineering (IJCISE) 1(4), 203–206 (2007)Google Scholar
  15. 15.
    Sluzek, A.: Using moment invariants to recognize and locate partially occluded 2d objects. Pattern Recognition Letters 7, 253 (1988)Google Scholar
  16. 16.
    Trier, O., Jain, A., Taxt, T.: Feature extraction methods for character recognition - a survey. Pattern Recognition 29(4), 641–662 (1996)CrossRefGoogle Scholar
  17. 17.
    Verma, B., Ghosh, M.: A neural-evolutionary approach for feature and architecture selection in online handwriting recognition. In: ICDAR ’03: Proceedings of the Seventh International Conference on Document Analysis and Recognition, Washington, DC, USA, pp. 1038–1042. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sara Izadi
    • 1
  • Ching Y. Suen
    • 1
  1. 1.Concordia UniversityMontrealCanada

Personalised recommendations