Handwritten Digit Recognition with a Back-Propagation Network
Abstract
We present an application of back-propagation networks to hand(cid:173) written digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.
Cite
Text
LeCun et al. "Handwritten Digit Recognition with a Back-Propagation Network." Neural Information Processing Systems, 1989.Markdown
[LeCun et al. "Handwritten Digit Recognition with a Back-Propagation Network." Neural Information Processing Systems, 1989.](https://mlanthology.org/neurips/1989/lecun1989neurips-handwritten/)BibTeX
@inproceedings{lecun1989neurips-handwritten,
title = {{Handwritten Digit Recognition with a Back-Propagation Network}},
author = {LeCun, Yann and Boser, Bernhard E. and Denker, John S. and Henderson, Donnie and Howard, R. E. and Hubbard, Wayne E. and Jackel, Lawrence D.},
booktitle = {Neural Information Processing Systems},
year = {1989},
pages = {396-404},
url = {https://mlanthology.org/neurips/1989/lecun1989neurips-handwritten/}
}