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 2^{nd<\/sup> 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.<\/p>\r\n","references":"[1]\tJ. Wood, \"Invariant pattern recognition: A review,\" Pattern Recognition, vol. 29, no. 1, 1996, 1-17.\r\n[2]\tAnil, K. Jain,Robert P. W. Duin, and Jianchang Mao: \u201cStatistical Pattern Recognition: A Review\u201d, IEEE Pattern Analysis and Machine Intelligence, vol 22, No. 1, PP 4-37, 2000. \r\n[3]\tP. F. Baldi and K. Hornik, \"Learning in linear neural networks: A survey,\" IEEE Transactions on Neural Networks, vol. 6, no. 4, 1995, 837-858.\r\n[4]\tT. Y. Kong and A. Rosenfeld, \"Digital topology: introduction and survey,\" Computer Vision, Graphics, and Image Processing, vol. 48, no. 3, pp. 357-393, 1989.\r\n[5]\tT. 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.\r\n[6]\tS. 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.\r\n[7]\tLorigo L. M., Govindaraju V., \u201cOffline Arabic Handwriting Recognition: A Survey\u201d, IEEE Trans Pattern Anal Mach Intelligence, Vol: 28(5): PP 712-24, 2006.\r\n[8]\tIshani Patel, Virag Jagtap, Ompriya Kale, \u201cA Survey on Feature Extraction Methods for Handwritten Digits Recognition\u201d, International Journal of Computer Applications, Vol 107, No 12, PP 11-17, 2014.\r\n[9]\tMuhammad Sharif, Farah Naz, Mussarat Yasmin, Muhammad Alyas Shahid, Amjad Rehman, \u201cFace Recognition: A Survey\u201d, Journal of Engineering Science and Technology Review, Vol 10, No 2, PP 166-177, 2017.\r\n[10]\tRafiqul Zaman Khan, Noor Adnan Ibraheem, \u201cHand Gesture Recognition: A Literature Review \u201cInternational Journal of Artificial Intelligence & Applications, Vol.3, No.4, pp 161-174, 2012.\r\n[11]\tBruce George Lindsay, Mary L. Lesperance , \u201cA review of semiparametric mixture models\u201d, Journal of Statistical Planning and Inference, Vol: 47 , No: 1 PP 29-39 , 1995.\r\n[12]\tChristopher M. Bishop, \u201cNeural Networks for Pattern Recognition\u201d, Clarendon Press, 1995, ISSN 0198538642.\r\n[13]\tA. Dempster, N. Laird, D. Rubin, Maximum likelihood from incomplete data via the em algorithm, J. Roy. Statist. Soc. Ser. 39 (1977) 1\u201338.\r\n[14]\tJ. Brendan, N. Jojic, Estimating mixture models of images and inferring spatial transformations using the em algorithm, IEEE Comput. Vision Pattern Recognition 2 (1999) 416\u2013422.\r\n[15]\tC. Bishop, J. Winn, Non-linear Bayesian image modelling, Proceedings of Sixth European.\r\n[16]\tN. Vasconcelos, A. Lippman, A probabilistic architecture for content-based image retrieval, Proceedings of International Conference on Computer Vision and Pattern Recognition, 2000, pp. 216\u2013221.\r\n[17]\tB. North, A. Blake, Using expectation-maximisation to learn dynamical models from visual data, Image Vision Computing. 17 (8) (1999) 611\u2013616.\r\n[18]\tM. Revow, C. Williams, G. E. Hinton, Using generative models for handwritten digit recognition, IEEE Trans. Pattern Anal. Mach. Intell. 20 (2) (1996) 592\u2013606.\r\n[19]\tChristopher M. Bishop, Pattern Recognition and Machine Learning, Springer Science, and Business Media, 2006.\r\n[20]\tT. Cootes, C. Taylor, A mixture model for representing shape variations. Image and Vision Computing, 17(1999) 403-409.\r\n[21]\tAl Shaher Abdullah, Hancock Edwin, Learning mixtures of Point Distribution models with the EM algorithm. Pattern Recognition, 36(2003) 2805-2818.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 144, 2018"}}