This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Integration of Structural and Statistical Information for Unconstrained Handwritten Numeral Recognition
March 1999 (vol. 21 no. 3)
pp. 263-270

Abstract—In this paper, we propose an approach that integrates the statistical and structural information for unconstrained handwritten numeral recognition. This approach uses state-duration adapted transition probability to improve the modeling of state-duration in conventional HMMs and uses macro-states to overcome the difficulty in modeling pattern structures by HMMs. The proposed method is superior to conventional approaches in many aspects. In the statistical and structural models, the orientations are encoded into discrete codebooks and the distributions of locations are modeled by joint Gaussian distribution functions. The experimental results show that the proposed approach can achieve high performance in terms of speed and accuracy.

[1] J. Rocha and T. Pavlidis, "A Shape Analysis Model With Applications to a Character Recognition System," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, pp. 393-404, 1994.
[2] S.W. Lee, "Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural Network," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 648-652, June 1996.
[3] S.B. Cho, “Neural Network Classifiers for Totally Unconstrained Handwritten Numerals,” IEEE Trans. Neural Networks, vol. 8, pp. 43-53, 1997.
[4] R.J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches.New York: John Wiley and Sons, 1992.
[5] R. Buse, Z.Q. Liu, and T. Caelli, "A Structural and Relational Approach to Handwritten Word Recognition," IEEE Trans. Systems, Man, and Cybernetics, Part B, vol. 27, no. 5, pp. 847-861, Oct. 1997.
[6] J. Hu, M.K. Brown, and W. Turin, “HMM Based On-Line Handwriting Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1,039-1,044, Oct. 1996.
[7] A.J. Elms, "The Representation and Recognition of Text Using Hidden Markov Models," PhD thesis, Dept. of Electronic and Electrical Eng., Univ. of Surrey, 1996.
[8] G. Kim and V. Govindaraju, “A Lexicon Driven Approach to Handwritten Word Recognition for Real Time Applications,“ IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 366-379, Apr. 1997.
[9] E. Persoon and K.S. Fu, "Shape Discrimination Using Fourier Descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 3, pp. 388-397, May 1986.
[10] S.O. Belkasim,M. Shridhar,, and M. Ahmadi,“Pattern recognition with moment invariants: A comparative study and new results,” Pattern Recognition, vol. 24, pp. 1117-1138, 1991.
[11] T. Pavlidis, Structural Pattern Recognition.Berlin: Springer-Verlag, 1977.
[12] C. Yüceer and K. Oflazer, "A Rotation, Scaling, and Translation Invariant Pattern Classification System," Pattern Recognition, vol. 25, no. 5, pp. 687-710, 1993.
[13] T. Pavlidis and F. Ali, "Computer Recognition of Handwritten Numerals by Polygonal Approximations," IEEE Trans. Systems, Man, and Cybernetics, vol. 5, pp. 610-614, Nov. 1975.
[14] D. Yu and H. Yan, "An Efficient Algorithm for Smoothing, Linearization and Detection of Structural Feature Points of Binary Image Contours," Pattern Recognition, vol. 30, no. 1, pp. 57-60, 1997.
[15] L. Lam, S.W. Lee, and C.Y. Suen, “Thinning Methodologies: A Comprehensive Survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, pp. 869-885, 1992.
[16] W.K. Pratt, Digital Image Processing. Wiley-Interscience, 1991.
[17] Ø.D. Trier, A.K. Jain, and T. Taxt, "Feature Extraction Methods for Character Recognition—A Survey," Pattern Recognition, vol. 29, no. 4, pp. 641-662, Apr. 1996.
[18] H. Freeman, "Boundary Encoding and Processing," B.S. Lipkin and A. Rosenfeld, eds., Picture Processing and Psychopictorics.New York: Academic Press, 1970, pp. 241-306.
[19] H.S. Park and S.W. Lee, "Off-Line Recognition of Large-Set Handwritten Characters With Multiple Hidden Markov Models," Pattern Recognition, vol. 29, no. 2, pp. 231-244, 1996.
[20] L.R. Rabiner, “Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257-285, 1989.
[21] A.J. Viterbi, “Error Bounds for Convolution Codes and an Asymptotically Optimum Decoding Algorithm,” IEEE Trans. Information Theory, vol. 13, pp. 260-269, 1967.
[22] S.E. Levinson, “Continuously Variable Duration Hidden Markov Models for Automatic Speech Recognition,“ Computers, Speech, and Language, vol. 1, pp. 29-45, Mar. 1986.
[23] S.V. Vaseghi, "State Duration Modeling in Hidden Markov Models," Signal Processing, vol. 42, pp. 31-41, 1995.
[24] L.R. Rabiner, B.H. Juang, S.E. Levinson, and M.M. Sondhi, "Recognition of Isolated Digits Using Hidden Markov Models With Continuous Mixture Densities," AT&T Technical J., vol. 64, no. 6, pp. 1,211-1,222, July-Aug. 1986.
[25] K.S. Fu, Syntactic Pattern Recognition and Applications.Englewood Cliffs, N.J.: Prentice Hall, 1982.
[26] H. Nishida and S. Mori, "An Algebraic Approach to Automatic Construction of Structural Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 12, pp. 1,298-1,311, Dec. 1993.
[27] Y.S. Hwang and S.Y. Bang, "Recognition of Unconstrained Handwritten Numerals by a Radial Basis Function Neural Network Classifier," Pattern Recognition Letters, vol. 18, pp. 657-664, 1997.
[28] T.M. Ha and H. Bunke, “On-Line, Handwritten Numeral Recognition by Perturbation Method,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 535-539, May 1997.
[29] K. Yamamoto, H. Yamada, and T. Saito, "Current State of Recognition Method for Japanese Characters and Database for Research of Handprinted Character Recognition," S. Impedovo and J.C. Simon, eds., From Pixels to Features III: Frontiers in Handwriting Recognition.New York: Elsevier Science, 1992, pp. 105-116.

Index Terms:
Handwritten numeral recognition, hidden Markov model, structural model, hybrid classifiers, outer contours, chain code-based features, macro-states.
Citation:
Jinhai Cai, Zhi-Qiang Liu, "Integration of Structural and Statistical Information for Unconstrained Handwritten Numeral Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 3, pp. 263-270, March 1999, doi:10.1109/34.754622
Usage of this product signifies your acceptance of the Terms of Use.