article

Backpropagation applied to handwritten zip code recognition

Authors:

L. D. JackelAuthors Info & Claims

Neural Computation, Volume 1, Issue 4

Pages 541 - 551

https://doi.org/10.1162/neco.1989.1.4.541

Published: 01 December 1989 Publication History

Abstract

The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.

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Denker, J. S., Gardner, W. R., Graf, H. P., Henderson, D., Howard, R. E., Hubbard, W., Jackel, L. D., Baird, H. S., and Guyon, I. 1989. Neural network recognizer for hand-written zip code digits. In D. Touretzky, ed., Advances in Neural Information Processing Systems, pp. 323-331. Morgan Kaufmann, San Mateo, CA.

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cover image Neural Computation

Neural Computation Volume 1, Issue 4

Winter 1989

149 pages

ISSN:0899-7667

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MIT Press

Cambridge, MA, United States

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Published: 01 December 1989

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