# Backpropagation Applied to Handwritten Zip Code Recognition
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## Abstract

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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.

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<img src="https://user-images.githubusercontent.com/26739999/142561080-cd1c4bdc-8739-46ca-bc32-76d462a32901.png" width="50%"/>
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## Citation
```latex
@ARTICLE{6795724,
  author={Y. {LeCun} and B. {Boser} and J. S. {Denker} and D. {Henderson} and R. E. {Howard} and W. {Hubbard} and L. D. {Jackel}},
  journal={Neural Computation},
  title={Backpropagation Applied to Handwritten Zip Code Recognition},
  year={1989},
  volume={1},
  number={4},
  pages={541-551},
  doi={10.1162/neco.1989.1.4.541}}
}
```
