Abstract
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.
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Keywords
- 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.
References
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)
Landay, J.A., Myers, B.A.: Sketching Interfaces: toward more human interface design. IEEE Computer 34(3), 56–64 (2001)
Rubine, D.: Specifying gestures by example. Computer Graphics 25, 329–337 (1991)
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)
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)
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)
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)
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)
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)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
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)
Jianying, H., Brown, M.K., Turin, W.: HMM-based online handwriting recognition. IEEE Transactions on PAMI 18(10), 1039–1045 (1996)
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)
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)
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Sun, Z., Jiang, W., Sun, J. (2006). Adaptive Online Multi-stroke Sketch Recognition Based on Hidden Markov Model. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_99
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DOI: https://doi.org/10.1007/11739685_99
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