Neural network models and their application to handwritten digit recognition

Abstract

Several neural network paradigms are discussed, and their application to the recognition of handwritten digits is considered. In particular, linear autoassociative systems, threshold logic networks, background error propagation models, Hopfield networks, and Boltzmann machines are considered. An explanation of each technique is presented and its application to digit recognition is discussed. The tradeoffs of time and space complexity versus recognition accuracy are considered. The objective is to determine the applicability of these techniques to the real-world need of the United States Postal Service (USPS) for a high-accuracy handwritten digit recognition algorithm. Recognition experiments are presented that were performed on a database of over 10000 handwritten digits that were extracted from live mail in a USPS mail-processing facility. The time required by each method and the recognition rates of the methods are discussed.