{"references": ["1.\tBlake, C., Keogh, E., & Merz, C. J. (1998). UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. . 2.\tCawley, G., & Talbot, N. (2005). Constructing Bayesian formulations of sparse kernel learning methods. Neural Networks, 18, 674\u2013683. 3.\tChen, J. H., & Chen, C. S. (2004). Reducing SVM classification time using multiple mirror classifiers. IEEE Transaction on Systems, Man,and Cybernetics, 34(2), 173\u20131183. 4.\tCho, S. B. (1997). Neural-network classifiers for recognizing totally unconstrained handwritten numerals. IEEE Transaction on Neural Networks, 8(1), 43\u201353. 5.\tCristianini, N., & Taylor, J. S. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press. 6.\tDong, J. X., Krzyzak, A., & Suen, C. Y. (2005). Fast SVM training algorithm with decomposition on very large data sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4), 603\u2013618. 7.\tEr, M. J., Wu, S., & Toh, H. L. (2002). Face recognition with RBF neural networks. IEEE Transaction on Neural Networks, 13(3), 697\u2013710. 8.\tFukunaga, K. (1990). Introduction to statistical pattern recognition (2nd ed.). New York: Academic Press. 9.\tFukunaga, K., & Kessell, D. L. (1971). Estimation of classification error. IEEE Transaction on Computers, 20(12), 1521\u20131527. 10.\tGagne, C., & Parizeau, M. (2007). Coevolution of nearest neighbor classifiers. International Journal of Pattern Recognition and Artificial Intelligence, 21(5), 921\u2013946. 11.\tHamamoto, Y., Uchimura, S., & Tomita, S. (1997). A bootstrap technique for nearest neighbor classifier design. IEEE Transaction on Pattern Analysis and Machine Intelligence, 19(1), 73\u201379."]}

due to the magnitude of the neural network science and MATLAB in terms of tools and algorithms, we will present a simple algorithm and explain it in general in this project, on the other hand due to we do not have enough knowledge in this field. In this project we will provide an overview of the role of handwriting recognition in neural network by using Optical Character Recognition algorithm.