Backpropagation Applied to Handwritten Zip Code Recognition
Yann LeCun, Bernhard E. Boser, John S. Denker, D. Henderson, Richard E. Howard, W. Hubbard, L. D. Jackel · Neural Computation · 1989
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.