Optical character recognition: A technology driver for neural networks

R. E. Howard, B. Boser, J. S. Denker, H. P. Graf, D. Henderson, W. Hubbard, L. D. Jackel, Y. Le Cun, H. S. Baird

Research output: Contribution to journalConference article

Abstract

It is shown that a neural net can perform handwritten digit recognition with state-of-the-art accuracy. The solution required automatic learning and generalization from thousands of training examples and also required designing into the system considerable knowledge about the task--neither engineering nor learning from examples alone would have sufficed. The resulting network is well suited for implementation on workstations or PCs and can take advantage of digital signal processors (DSPs) or custom VLSI.

Original languageEnglish (US)
Pages (from-to)2433-2436
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume3
StatePublished - 1990
Event1990 IEEE International Symposium on Circuits and Systems Part 3 (of 4) - New Orleans, LA, USA
Duration: May 1 1990May 3 1990

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Optical character recognition: A technology driver for neural networks'. Together they form a unique fingerprint.

  • Cite this

    Howard, R. E., Boser, B., Denker, J. S., Graf, H. P., Henderson, D., Hubbard, W., Jackel, L. D., Le Cun, Y., & Baird, H. S. (1990). Optical character recognition: A technology driver for neural networks. Proceedings - IEEE International Symposium on Circuits and Systems, 3, 2433-2436.