Abstract
This paper introduces a new stochastic framework of modeling sequences of features that are combinations of discrete symbols and continuous attributes. Unlike traditional hidden Markov models, the new model emits observations on transitions instead of states. In this framework, a feature is first labeled with a symbol and then a set of featuredependent continuous attributes is associated to give more details of the feature. This two-level hierarchy is modeled by symbol observation probabilities which are discrete and attribute observation probabilities which are continuous. The model is rigorously defined and the algorithms for its training and decoding are presented. This framework has been applied to off-line handwritten word recognition using high-level structural features and proves its effectiveness in experiments.
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References
H. Bunke, M. Roth, and E. Schukat-Talamazzini, “Off-line cursive handwriting recognition using hidden Markov models,” Pattern Recognition, vol. 28, no. 9, pp. 1399–1413, 1995.
M. Chen, A. Kundu, and S. Srihari, “Variable duration hidden Markov model and morphological segmentation for handwritten word recognition,” IEEE Transactions on Image Processing, vol. 4, pp. 1675–1688, December 1995.
M. Mohammed and P. Gader, “Handwritten word recognition using segmentationfree hidden Markov modeling and segmentation-based dynamic programming techniques,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 548–554, May 1996.
A. Senior and A. Robinson, “An off-line cursive handwriting recognition system,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 309–321, 1998.
A. El-Yacoubi, M. Gilloux, R. Sabourin, and C. Y. Suen, “An HMM-based approach for off-line unconstrained handwritten word modeling and recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 752–760, August 1999.
H. Xue and V. Govindaraju, “Building skeletal graphs for structural feature extraction on handwriting images,” in International Conference on Document Analysis and Recognition, (Seattle, Washington), pp. 96–100, September 2001.
L. Baum, “An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes,” Inequalities, vol. 3, pp. 1–8, 1972.
M. Chen, Handwritten Word Recognition Using Hidden Markov Models. PhD thesis, State University of New York at Buffalo, September 1993.
G. Kim and V. Govindaraju, “A lexicon driven approach to handwritten word recognition for real-time applications,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 366–379, April 1997.
S. Tulyakov and V. Govindaraju, “Probabilistic model for segmentation based word recognition with lexicon,” in Proceedings of Sixth International Conference on Document Analysis and Recognition, (Seattle), pp. 164–167, September 2001.
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© 2002 Springer-Verlag Berlin Heidelberg
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Xue, H., Govindaraju, V. (2002). A Stochastic Model Combining Discrete Symbols and Continuous Attributes and Its Application to Handwriting Recognition. In: Lopresti, D., Hu, J., Kashi, R. (eds) Document Analysis Systems V. DAS 2002. Lecture Notes in Computer Science, vol 2423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45869-7_10
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DOI: https://doi.org/10.1007/3-540-45869-7_10
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