Reading Handwritten Digits: A ZIP Code Recognition System

Authors:
Ofer Matan;Henry S. Baird;Jane Bromley;Christopher J. C. Burges;John S. Denker;Lawrence D. Jackel;Yann Le Cun;Edwin P. D. Pednault;William D. Satterfield;Charles E. Stenard;Timothy J. Thompson
Affiliations:
-;-;-;-;-;-;-;-;-;-;-
Venue:
Computer
Year:
1992

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Abstract

A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail.