Handwritten objects recognition using Regularized Logistic Regression and feedforward Neural Networks

In this paper, we present a feedforward training algorithm using Regularized Logistic Regression and Neural Networks to recognize handwritten objects. Furthermore, we intend to consider the effect of Gaussian noise in this procedure in order to examine the versatility of our approach. We might intend to transmit the image of our digits through an AWGN channel to a certain destination and then do the recognition process in our destination, so we need our algorithm to be still robust against the noises caused by AWGN channels and sensors. The main advantage of our approach is to reduce the amount of computations and, in turn, considerably decrease the processing time.