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Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
July 2008 (vol. 30 no. 7)
pp. 1121-1131
ASCII Text
x 
 
Neila Mezghani, Amar Mitiche, Mohamed Cheriet, "Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1121-1131, July, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.70753,
author = {Neila Mezghani and Amar Mitiche and Mohamed Cheriet},
title = {Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {7},
issn = {0162-8828},
year = {2008},
pages = {1121-1131},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70753},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
IS - 7
SN - 0162-8828
SP1121
EP1131
EPD - 1121-1131
A1 - Neila Mezghani,
A1 - Amar Mitiche,
A1 - Mohamed Cheriet,
PY - 2008
KW - Clustering
KW - classification
KW - and
KW - association rules
KW - Associative memories
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
This study investigates Bayes classification of online Arabic characters represented by histograms of tangent differences and Gibbs modeling of the class-conditional probability density functions. The parameters of these Gibbs density functions are estimated following the Zhu, Wu, and Mumford constrained maximum entropy formalism, originally introduced for image and shape synthesis. We investigate two partition function estimation methods: one uses the training sample and the other draws from a reference distribution. The efficiency of the corresponding Bayes decision methods, and of a combination of these, is shown in experiments using a database of 9504 freely written samples by 22 scriptors. Comparisons to the nearest neighbor rule method and Kohonen neural network methods are provided.

[1] B. Al-Badr and S.A. Mahmoud, “Survey and Bibliography of Arabic Optical Text Recognition,” Signal Processing, vol. 41, no. 1, pp. 49-77, 1995.
[2] T.S. Al-Sheikh and S.G. El-Taweel, “Proc. Real-Time Arabic Handwritten Character Recognition,” Pattern Recognition, vol. 23, no. 12, pp. 1323-1332, 1990.
[3] A.M. Alimi, “An Evolutionary Neuro-Fuzzy Approach to Recognize On-Line Arabic Handwritting,” Proc. Int'l Conf. Document Analysis and Recognition, vol. 1, pp. 382-386, Aug. 1997.
[4] A.M. Alimi, “A Neuro-Fuzzy Approach to Recognize On-Line Arabic Handwritting,” Proc. Int'l Conf. Neural Networks, vol. 3, pp.1397-1400, Aug. 1997.
[5] N. Ben Amara and F. Bouslama, “Classification of Arabic Script Using Multiple Sources of Information: State of the Art and Perspectives,” Int'l J. Document Analysis and Recognition, vol. 5, no. 4, pp. 195-212, July 2003.
[6] A. Amin, “Off Line Arabic Character Recognition—A Survey,” Proc. Int'l Conf. Document Analysis and Recognition, vol. 1, p. 596, Aug. 1997.
[7] A. Amin, “Off-Line Arabic Character-Recognition: The State of the Art,” Pattern Recognition, vol. 31, no. 5, pp. 517-530, May 1998.
[8] G.R. Ball, S.N. Srihari, and H. Srinivasan, “Segmentation-Based and Segmentation-Free Methods for Spotting Handwritten Arabic Words,” Proc. Int'l Workshop Frontiers of Handwriting Recognition, Oct. 2006.
[9] F. Biadsy, J. El-Sana, and N. Habash, “Online Arabic Handwriting Recognition Using Hidden Markov Models,” Proc. Int'l Workshop Frontiers of Handwriting Recognition, Oct. 2006.
[10] F. Bouslama and A. Amin, “Pen-Based Recognition System of Arabic Character Utilizing Structural and Fuzzy Techniques,” Proc. Second Int'l Conf. Knowledge-Based Intelligent Electronic Systems, pp. 76-85, 1998.
[11] X. Descombes, R. Morris, J. Zerubia, and M. Berthod, “Maximum Likelihood Estimation of Markov Random Field Parameters Using Markov Chain Monte Carlo Algorithms,” Proc. Int'l Workshop Energy Minimization Methods, May 1997.
[12] D. Doermann, “The Indexing and Retrieval of Document Images: A Survey,” Computer Vision and Image Processing, vol. 70, no. 3, pp.287-298, June 1998.
[13] P.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, 2001.
[14] M.S. El-wakil and A.A. Shoukry, “On-Line Recognition of Handwritten Arabic Character Recognition,” Pattern Recognition, vol. 22, no. 2, pp. 97-105, 1989.
[15] S. Al Emami and M. Usher, “On-Line Recognition of Handwritten Arabic Characters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 704-710, July 1990.
[16] S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans. Pattern Recognition and Machine Intelligence, vol. 6, no. 6, pp. 721-741, 1984.
[17] V.K. Govindan and A.P. Shivaprasad, “Character Recognition —A Review,” Pattern Recognition, vol. 23, no. 7, pp. 671-683, 1990.
[18] A.H. Hassin, X.L. Tang, J.F. Liu, and W. Zhao, “Printed Arabic Character Recognition Using HMM,” J. Computer Science Technology, vol. 19, no. 4, pp. 538-543, 2004.
[19] E.T. Jaynes, “Information Theory and Statistical Mechanics I,” Physical Rev., vol. 106, pp. 620-630, 1957.
[20] T. Jebara and T. Jaakkola, “Feature Selection and Dualities in Maximum Entropy Discrimination,” Uncertainity in Artificial Intelligence, 2000.
[21] S. Jehan-Besson, A. Herbulot, M. Barlaud, and G. Aubert, Shape Gradient for Image and Video Segmentation, Math. Models in Computer Vision: The Handbook, 2005.
[22] M. Jianchang, “Neural Networks in Off-Line Text Recognition: A Review,” Proc. Int'l Joint Conf. Neural Networks, vol. 4, pp. 2934-2939, July 1999.
[23] M.S. Khorsheed, “Off-Line Arabic Character Recognition—A Review,” Pattern Analysis and Applications, vol. 5, no. 1, pp. 31-45, 2002.
[24] T. Kohonen, Self-Organizing Maps. Springer, 1995.
[25] C.L. Liu, S. Jaeger, and M. Nakagawa, “Online Recognition of Chinese Characters: The State-of-the-Art,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 198-213, Feb. 2004.
[26] G. Lorette, “Handwriting Recognition or Reading? What Is the Situation at the Dawn of the 3rd Millenium,” Int'l J. Document Analysis and Recognition, vol. 2, no. 1, pp. 2-12, 1999.
[27] L.M. Lorigo and V. Govindaraju, “Offline Arabic Handwriting Recognition: A Survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 712-724, May 2006.
[28] M.A. Mahjoub, “Apport de la modélisation de la durée d'état dans la reconnaissance en-ligne des caractères arabes par HMMs,” Proc. 17th Journée Tunisienne en Électrotechnique et Automatique, pp. 341-348, 1997.
[29] S.A. Mahmoud, “Arabic Character-Recognition Using Fourier Descriptors and Character Contour Encoding,” Pattern Recognition, vol. 27, no. 6, pp. 815-824, June 1994.
[30] S. Marukatat, R. Sicard, T. Artières, and P. Gallinari, “A Flexible Recognition Engine for Complex On-Line Handwritten Character Recognition,” Proc. Seventh Int'l Conf. Document Analysis and Recognition, vol. 2, pp. 1048-1051, Aug. 2003.
[31] N. Mezghani, M. Cheriet, and A. Mitiche, “Combination of Pruned Kohonen Maps for On-Line Arabic Characters Recognition,” Proc. Seventh Int'l Conf. Document Analysis and Recognition, vol. 2, pp. 900-905, Aug. 2003.
[32] N. Mezghani, A. Mitiche, and M. Cheriet, “On-Line Recognition of Handwritten Arabic Characters Using a Kohonen Neural Network,” Proc. Eighth Int'l Workshop Frontiers in Handwriting Recognition, pp. 490-495, 2002.
[33] N. Mezghani, A. Mitiche, and M. Cheriet, “Reconnaissance En-Ligne de Caractères Arabes Manuscrits par un Rèseau de Kohonen,” Proc. Vision Interface, pp. 186-191, 2002.
[34] N. Mezghani, A. Mitiche, and M. Cheriet, “A New Representation of Shape and Its Use for Superior Performance in On-Line Arabic Character Recognition by an Associative Memory,” Int'l J. Document Analysis and Recognition, vol. 7, no. 4, pp. 201-210, 2005.
[35] A. Mitiche and J.K. Aggarwal, “Pattern Category Assignment by Neural Networks and the Nearest Neighbors Rule,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 10, pp. 393-408, 1996.
[36] A. Mitiche and M. Lebidoff, “Pattern Classification by a Condensed Neural Networks,” Neural Network, vol. 14, no. 4, pp.575-580, May 2001.
[37] I.S. Oh, J.S. Lee, and B.R. Moon, “Hybrid Genetic Algorithms for Feature Selection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1424-1437, Nov. 2004.
[38] G. Olivier, H. Miled, K. Romeo, and Y. Lecourtier, “Segmentation and Coding of Arabic Handwritten Words,” Proc. Int'l Conf. Pattern Recognition, vol. 3, pp. 264-268, 1996.
[39] V. Onnia, M. Tico, and J. Saarinen, “Feature Selection Method Using Neural Network,” Proc. Int'l Conf. Image Processing, vol. 1, pp. 513-516, 2001.
[40] R. Plamondon, “A Model-Based Segmentation Framework for Computer Processing of Handwriting,” Proc. 11th IAPR Int'l Conf. Pattern Recognition, pp. 303-307, 1992.
[41] R. Plamondon, “Handwriting Generation: The Delta Lognormal Theory,” Proc. Fourth Int'l Workshop Frontiers in Handwriting Recognition, pp. 1-10, 1994.
[42] R. Plamondon and S.N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, Jan. 2000.
[43] G. Potamianos and J. Goutsias, “Stochastic Approximation Algorithms for Partition Function Estimation of Gibbs Random Fields,” IEEE Trans. Information Theory, vol. 43, no. 6, pp. 1948-1965, 1997.
[44] K.R. Pakker, A. Ameur, C. Olivier, and Y. Lecourtier, “Structural-Analysis of Arabic Handwriting: Segmentation and Recognition,” Machine Vision and Applications, vol. 8, no. 4, pp. 232-240, 1995.
[45] M. Sabourin, A. Mitiche, D. Thomas, and G. Nagy, “NN 1 or Hand-Printed Digit Recognition Using Nearest Neighbors,” Proc. Second Ann. Symp. Document Analysis and Information Retrieval, pp.397-412, Apr. 1993.
[46] M. Schenkel, I. Guyon, and D. Henderson, “On-Line Script Recognition Using Time Delay Neural Networks and Hidden Markov Models,” Proc. Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 2, pp. 637-640, 1994.
[47] J. Shah, “Minimax Entropy and Learning by Diffusion,” Proc. Int'l Conf. Computer Vision, pp. 92-97, 1998.
[48] K. Shima, M. Todoriki, and A. Suzuki, “SVM-Based Feature Selection of Latent Semantic Features,” Pattern Recognition Letters, vol. 25, no. 9, pp. 1051-1057, July 2004.
[49] J.C. Spall, “Estimation via Markov Chain Monte Carlo,” IEEE Control Systems Magazine, vol. 23, pp. 34-45, 2003.
[50] C.C. Tappert, C.Y. Suen, and T. Wakahara, “Online Handwriting Recognition—A Survey,” Proc. Int'l Conf. Pattern Recognition, vol. 2, pp. 1123-1132, Nov. 1988.
[51] C.C. Tappert, C.Y. Suen, and T. Wakahara, “The State of the Art in On-Line Handwriting Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 8, pp. 787-808, Aug. 1990.
[52] O. Trier, A. Jain, and T. Taxt, “Feature Extraction Methods for Character Recognition—A Survey,” Pattern Recognition, vol. 29, pp. 641-662, 1996.
[53] A.M. Zeki, “The Segmentation Problem in Arabic Character Recognition the State of the Art,” Proc. First Int'l Conf. Information and Communication Technologies, pp. 11-26, Aug. 2005.
[54] L. Zheng, A.H. Hassin, and X. Tang, “A New Algorithm for Machine Printed Arabic Character Segmentation,” IEEE Pattern Recognition Letters, vol. 25, no. 15, pp. 1723-1729, Nov. 2004.
[55] S.C. Zhu, Y. Wu, and D. Mumford, “Minimax Entropy Principle and Its Application to Texture Modeling,” Neural Computer, vol. 9, no. 9, pp. 1627-1660, 1997.
[56] S.C. Zhu, Y. Wu, and D. Mumford, “Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling,” Int'l J. Computer Vision, vol. 27, no. 2, pp. 107-126, 1998.
[57] S.C. Zhu, “Embedding Gestalt Laws in Markov Random Fields,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1170-1187, Nov. 1999.

Index Terms:
Clustering, classification, and, association rules, Associative memories
Citation:
Neila Mezghani, Amar Mitiche, Mohamed Cheriet, "Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1121-1131, July 2008, doi:10.1109/TPAMI.2007.70753
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