Handwritten zip code recognition with multilayer networks

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

Research output: Contribution to journalConference articlepeer-review


An application of back-propagation networks to handwritten zip-code recognition is presented. Minimal preprocessing of the data is required, but the architecture of the network is highly constrained and specifically designed for the task. The input of the network consists of size-normalized images of isolated digits. The performance on zip-code digits provided by the US Postal Service is 92% recognition, 1% substitution, and 7% rejects. Structured neural networks can be viewed as statistical methods with structure which bridge the gap between purely statistical and purely structural methods.

Original languageEnglish (US)
Pages (from-to)35-40
Number of pages6
JournalProceedings - International Conference on Pattern Recognition
StatePublished - 1990
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: Jun 16 1990Jun 21 1990

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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