Handprinted digit recognition using spatiotemporal connectionist models

Abstract

A connectionist model for recognizing unconstrained handprinted digits is described. Instead of treating the input as a static signal, the image is canned over time and converted into a time-varying signal. The temporalized image is processed by a spatiotemporal connectionist network. The resulting system offers shift-invariance along the temporalized axis, a reduction in the number of free parameters, and the ability to process images of arbitrary length. For a set of real-world ZIP code digit images, the system achieved a 99.1% recognition rate on the training set and a 96.0% recognition rate on the test with no rejections. A 99.0% recognition rate on the test set was achieved when 14.6% of the images were rejected.<>