@article{(Open Science Index):https://publications.waset.org/pdf/14364,
	  title     = {Arabic Character Recognition using Artificial Neural Networks and Statistical Analysis },
	  author    = {Ahmad M. Sarhan and  Omar I. Al Helalat},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. },
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {3},
	  year      = {2007},
	  pages     = {506 - 510},
	  ee        = {https://publications.waset.org/pdf/14364},
	  url   	= {https://publications.waset.org/vol/3},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 3, 2007},
	}