Backpropagation Applied to Handwritten Zip Code Recognition

doi:10.1162/neco.1989.1.4.541

Backpropagation Applied to Handwritten Zip Code Recognition

1989-12-01

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AI

DOI：10.1162/neco.1989.1.4.541

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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.

Backpropagation Applied to Handwritten Zip Code Recognition

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