Handwritten Digit Recognition with a Back-Propagation Network

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

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Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, R. Howard, Wayne Hubbard, Lawrence Jackel


We present an application of back-propagation networks to hand(cid:173) written digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.