Recognition of Isolated Handwritten Persian Characterizing Hamming Network

In this paper we propose a system for recognition of isolated handwritten Persian characters. A novel method that uses derivation has been used for feature extraction. Hamming network has been used for classification in this system. Hamming network is a neural network fully connected from input layer to all neuron in output layer which calculate amount of resemblance between input patterns than training patterns. The training and test patterns were gathered from dataset over 47965 patterns. The 32 characters in Persian language were categorized into 9 different classes which characters of each class are very similar to each other's. The Classification rate with this approach is about 95 percent and Recognition rate in each class is about 90 percent. The results show an increment in recognition rates in comparison with our previous work.