ML Anthology

Backpropagation Applied to Handwritten Zip Code Recognition

Yann LeCun, Bernhard E. Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne E. Hubbard, Lawrence D. Jackel
NeCo 1989 pp. 541-551
doi:10.1162/NECO.1989.1.4.541 /neco/1989/lecun1989neco-backpropagation/

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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Cite

Text

LeCun et al. "Backpropagation Applied to Handwritten Zip Code Recognition." Neural Computation, 1989. doi:10.1162/NECO.1989.1.4.541

Markdown

[LeCun et al. "Backpropagation Applied to Handwritten Zip Code Recognition." Neural Computation, 1989.](https://mlanthology.org/neco/1989/lecun1989neco-backpropagation/) doi:10.1162/NECO.1989.1.4.541

BibTeX

@article{lecun1989neco-backpropagation,
  title     = {{Backpropagation Applied to Handwritten Zip Code Recognition}},
  author    = {LeCun, Yann and Boser, Bernhard E. and Denker, John S. and Henderson, Donnie and Howard, Richard E. and Hubbard, Wayne E. and Jackel, Lawrence D.},
  journal   = {Neural Computation},
  year      = {1989},
  pages     = {541-551},
  doi       = {10.1162/NECO.1989.1.4.541},
  volume    = {1},
  url       = {https://mlanthology.org/neco/1989/lecun1989neco-backpropagation/}
}