Arabic Character Recognition Using Regression Curves with the Expectation Maximization Algorithm
Authors: Abdullah A. AlShaher
In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2363274Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 368
 J. Wood, "Invariant pattern recognition: A review," Pattern Recognition, vol. 29, no. 1, 1996, 1-17.
 Anil, K. Jain,Robert P. W. Duin, and Jianchang Mao: “Statistical Pattern Recognition: A Review”, IEEE Pattern Analysis and Machine Intelligence, vol 22, No. 1, PP 4-37, 2000.
 P. F. Baldi and K. Hornik, "Learning in linear neural networks: A survey," IEEE Transactions on Neural Networks, vol. 6, no. 4, 1995, 837-858.
 T. Y. Kong and A. Rosenfeld, "Digital topology: introduction and survey," Computer Vision, Graphics, and Image Processing, vol. 48, no. 3, pp. 357-393, 1989.
 T. R. Reed and J. M. H. Dubuf, "A review of recent texture segmentation and feature extraction techniques," CVGIP - Image Understanding, vol. 57, no. 3, pp. 359-372, 1993.
 S. Sarkar and K. L. Boyer, "Perceptual organization in computer vision - a review and a proposal for a classifactory structure", IEEE Transactions on Systems Man and Cybernetics, vol. 23, no. 2, 1993, 382-399.
 Lorigo L. M., Govindaraju V., “Offline Arabic Handwriting Recognition: A Survey”, IEEE Trans Pattern Anal Mach Intelligence, Vol: 28(5): PP 712-24, 2006.
 Ishani Patel, Virag Jagtap, Ompriya Kale, “A Survey on Feature Extraction Methods for Handwritten Digits Recognition”, International Journal of Computer Applications, Vol 107, No 12, PP 11-17, 2014.
 Muhammad Sharif, Farah Naz, Mussarat Yasmin, Muhammad Alyas Shahid, Amjad Rehman, “Face Recognition: A Survey”, Journal of Engineering Science and Technology Review, Vol 10, No 2, PP 166-177, 2017.
 Rafiqul Zaman Khan, Noor Adnan Ibraheem, “Hand Gesture Recognition: A Literature Review “International Journal of Artificial Intelligence & Applications, Vol.3, No.4, pp 161-174, 2012.
 Bruce George Lindsay, Mary L. Lesperance , “A review of semiparametric mixture models”, Journal of Statistical Planning and Inference, Vol: 47 , No: 1 PP 29-39 , 1995.
 Christopher M. Bishop, “Neural Networks for Pattern Recognition”, Clarendon Press, 1995, ISSN 0198538642.
 A. Dempster, N. Laird, D. Rubin, Maximum likelihood from incomplete data via the em algorithm, J. Roy. Statist. Soc. Ser. 39 (1977) 1–38.
 J. Brendan, N. Jojic, Estimating mixture models of images and inferring spatial transformations using the em algorithm, IEEE Comput. Vision Pattern Recognition 2 (1999) 416–422.
 C. Bishop, J. Winn, Non-linear Bayesian image modelling, Proceedings of Sixth European.
 N. Vasconcelos, A. Lippman, A probabilistic architecture for content-based image retrieval, Proceedings of International Conference on Computer Vision and Pattern Recognition, 2000, pp. 216–221.
 B. North, A. Blake, Using expectation-maximisation to learn dynamical models from visual data, Image Vision Computing. 17 (8) (1999) 611–616.
 M. Revow, C. Williams, G. E. Hinton, Using generative models for handwritten digit recognition, IEEE Trans. Pattern Anal. Mach. Intell. 20 (2) (1996) 592–606.
 Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer Science, and Business Media, 2006.
 T. Cootes, C. Taylor, A mixture model for representing shape variations. Image and Vision Computing, 17(1999) 403-409.
 Al Shaher Abdullah, Hancock Edwin, Learning mixtures of Point Distribution models with the EM algorithm. Pattern Recognition, 36(2003) 2805-2818.