DMCA
Handwritten Digit Recognition with a Back-Propagation Network (1990)
| Venue: | Advances in Neural Information Processing Systems |
| Citations: | 285 - 21 self |
BibTeX
@INPROCEEDINGS{Cun90handwrittendigit,
author = {Le Cun and B. Boser and J. S. Denker and D. Henderson and R. E. Howard and W. Hubbard and L. D. Jackel},
title = {Handwritten Digit Recognition with a Back-Propagation Network},
booktitle = {Advances in Neural Information Processing Systems},
year = {1990},
pages = {396--404},
publisher = {Morgan Kaufmann}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
We present an application of back-propagation networks to handwritten 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. 1 INTRODUCTION The main point of this paper is to show that large back-propagation (BP) networks can be applied to real image-recognition problems without a large, complex preprocessing stage requiring detailed engineering. Unlike most previous work on the subject (Denker et al., 1989), the learning network is directly fed with images, rather than feature vectors, thus demonstrating the ability of BP networks to deal with large amounts of low level information. Previous work performed on simple digit images (Le Cun, 1989) showed that the architecture of the network strongly...
Keyphrases
back-propagation network handwritten digit recognition previous work low level information u.s. postal service normalized image real image-recognition problem learning network digit recognition large back-propagation feature vector detailed engineering minimal preprocessing main point large amount error rate bp network le cun simple digit image reject rate isolated digit complex preprocessing stage zipcode digit
