Recognizing Hand-Printed Letters and Digits

Abstract

We are developing a hand-printed character recognition system using a multi(cid:173) layered neural net trained through backpropagation. We report on results of training nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a sty(cid:173) lus digitizer. Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing practical pattern recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy. From a scientific standpoint, these re(cid:173) sults raise doubts about the relevance to backpropagation of learning models that estimate the likelihood of high generalization from estimates of capacity. Reducing capacity does have other benefits however, especially when the re(cid:173) duction is accomplished by using local receptive fields with shared weights. In this latter case, we find the net evolves feature detectors resembling those in visual cortex and Linsker's orientation-selective nodes.