High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks

This manuscript considers a new architecture to handwritten characters recognition based on simulation of the behavior of one type of artificial neural network, called the Error Back Propagation Artificial Neural Network (EBPANN). We present an overview of our neural network to be optimized and tested on 12 offline isolated Arabic handwritten characters () because the similarity of some Arabic characters and the location of the points in the character. Accuracy of 93.61% is achieved using EBPANN which is the highest accuracy achieved during Offline Handwritten Arabic Character Recognition. It is noted that the EBPANN in general generates an optimized comparison between the input samples and database samples which improves the final recognition rate. Experimental results show that the EBPANN is convergent and more accurate in solutions that minimize the error recognition rate.