Design of neural nets for character recognition

In this paper, the possibility of using Artificial Neural Networks (ANNs) in the field of character recognition is discussed. Our study is undertaken on theoretical and practical investigations of two feedforward models (the Prototype Multilayer Perceptron (MLP) and the Fully Connected model) by using the backpropagation training algorithm. We introduce a fully connected network of three layers in order to make a classification between two characters T and C without being affected by shift in position, rotation, or scaling. A complete analog implementation is presented by using D-MOS transistors acting as synaptic weights and bipolar transistors to represent the nonlinear sigmoid function. Simulation results for fully connected networks are compared with those of traditional techniques (prototype MLP model) in order to recognize more characters.