Abstract: Handwritten character recognition is one of the focused areas of research in the field of Pattern Recognition. In this paper, a hybrid model of rough neural network has been developed for recognizing isolated Arabic/Farsi digital characters. It solves the neural network problems; proneness to overfitting, and the empirical nature of model development using rough sets and the dissimilarity analysis. Moreover the perturbation in the input data is violated using rough neuron. This paper describes an evolutionary rough neural network based technique to recognize Arabic/Farsi isolated handwritten digital characters. This method involves hierarchical feature extraction, data clustering and classification. In contrast with conventional neural network, a comparative study is appeared. Also, the details and limitations are discussed.

Elsayed Radwan, “Hybrid of Rough Neural Networks for Arabic/Farsi Handwriting Recognition” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(2), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020207

@article{Radwan2013,
title = {Hybrid of Rough Neural Networks for Arabic/Farsi Handwriting Recognition},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.020207},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020207},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {2},
author = {Elsayed Radwan}
}