Comparing different neural network architectures for classifying handwritten digits

Abstract

An evaluation is made of several neural network classifiers, comparing their performance on a typical problem, namely handwritten digit recognition. For this purpose, the authors use a database of handwritten digits, with relatively uniform handwriting styles. The authors propose a novel way of organizing the network architectures by training several small networks so as to deal separately with subsets of the problem, and then combining the results. This approach works in conjunction with various techniques including: layered networks with one or several layers of adaptive connections, fully connected recursive networks, ad hoc networks with no adaptive connections, and architectures with second-degree polynomial decision surfaces.<>