Neural Networks are being used for character recognition from last many years
but most of the work was confined to English character recognition. Till date,
a very little work has been reported for Handwritten Farsi Character
recognition. In this paper, we have made an attempt to recognize handwritten
Farsi characters by using a multilayer perceptron with one hidden layer. The
error backpropagation algorithm has been used to train the MLP network. In
addition, an analysis has been carried out to determine the number of hidden
nodes to achieve high performance of backpropagation network in the recognition
of handwritten Farsi characters. The system has been trained using several
different forms of handwriting provided by both male and female participants of
different age groups. Finally, this rigorous training results an automatic HCR
system using MLP network. In this work, the experiments were carried out on two
hundred fifty samples of five writers. The results showed that the MLP networks
trained by the error backpropagation algorithm are superior in recognition
accuracy and memory usage. The result indicates that the backpropagation
network provides good recognition accuracy of more than 80% of handwritten
Farsi characters.