Adaptive Online Multi-stroke Sketch Recognition Based on Hidden Markov Model

  • Zhengxing Sun
  • Wei Jiang
  • Jianyong Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


This paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model (HMM). The method views the drawing sketch as the result of a stochastic process that is governed by a hidden stochastic model and identified according to its probability of generating the output. To capture a user’s drawing habits, a composite feature combining both geometric and dynamic characteristics of sketching is defined for sketch representation. To implement the stochastic process of online multi-stroke sketch recognition, multi-stroke sketching is modeled as an HMM chain while the strokes are mapped as different HMM states. To fit the requirement of adaptive online sketch recognition, a variable state-number determining method for HMM is also proposed. The experiments prove both the effectiveness and efficiency of the proposed method.


Hide Markov Model Composite Feature Handwriting Recognition Handwritten Character Hide Markov Model State 
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.
    Sun, Z., Liu, J.: Informal user interfaces for graphical computing. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 675–682. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Landay, J.A., Myers, B.A.: Sketching Interfaces: toward more human interface design. IEEE Computer 34(3), 56–64 (2001)Google Scholar
  3. 3.
    Rubine, D.: Specifying gestures by example. Computer Graphics 25, 329–337 (1991)CrossRefGoogle Scholar
  4. 4.
    Newman, M.W., James, L., Hong, J.I., et al.: DENIM: An informal web site design tool inspired by observations of practice. HCI 18, 259–324 (2003)CrossRefGoogle Scholar
  5. 5.
    Fonseca, M.J., Pimentel, C., Jorge, J.A.: CALI - an online scribble recognizer for calligraphic interfaces. In: AAAI Spring Symposium on Sketch Understanding, pp. 51–58. AAAI Press, Menlo Park (2002)Google Scholar
  6. 6.
    Calhoun, C., Stahovich, T.F., Kurtoglu, T., et al.: Recognizing multi-stroke symbols. In: AAAI Spring Symposium on Sketch Understanding, pp. 15–23. AAAI Press, Menlo Park (2002)Google Scholar
  7. 7.
    Xu, X., Sun, Z., Peng, B., et al.: An online composite graphics recognition approach based on matching of spatial relation graphs. International Journal of Document Analysis and Recognition 7(1), 44–55 (2004)Google Scholar
  8. 8.
    Sun, Z., Liu, W., Peng, B., et al.: User adaptation for online sketchy shape recognition. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 305–316. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Sun, Z., Zhang, L., Tang, E.: An incremental learning algorithm based on SVM for online sketchy shape recognition. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 655–659. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  11. 11.
    Chien, J.-T.: On-line unsupervised learning of hidden Markov models for adaptive speech recognition. Proceedings of Vision, Image and Signal Processing 148(5), 315–324 (2001)CrossRefGoogle Scholar
  12. 12.
    Jianying, H., Brown, M.K., Turin, W.: HMM-based online handwriting recognition. IEEE Transactions on PAMI 18(10), 1039–1045 (1996)Google Scholar
  13. 13.
    Nakai, M., Akira, N., Shimodaira, H., et al.: Sub-stroke approach to HMM-based On-line Kanji Handwriting Recognition. In: International Conference on Document Analysis and Recognition, pp. 491–495 (2001)Google Scholar
  14. 14.
    Lee, J.J., Kim, J.W., Kim, J.H.: Data-driven design of HMM Topology for on-line handwriting recognition. In: Hidden Markov models: applications in computer vision. World Scientific Series, pp. 107–121 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhengxing Sun
    • 1
  • Wei Jiang
    • 1
  • Jianyong Sun
    • 1
  1. 1.State Key Lab for Novel Software TechnologyNanjing UniversityNanjing

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