Recognition of strings using nonstationary Markovian models: anapplication in ZIP code recognition
Bouchaffra, D.
Govindaraju, V.
Srihari, S.N.
Dept. of Comput. Sci., State Univ. of New York, Buffalo, NY;
This paper appears in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Publication Date: 1999
Volume: 2,
On page(s): -179 Vol. 2
Meeting Date: 06/23/1999 - 06/25/1999
Location: Fort Collins, CO, USA
ISBN: 0-7695-0149-4
References Cited: 11
INSPEC Accession Number: 6346905
Digital Object Identifier: 10.1109/CVPR.1999.784626
Current Version Published: 2002-08-06
Abstract
This paper presents nonstationary Markovian models and their
application to recognition of strings of tokens, such as ZIP codes in
the US mailstream. Unlike traditional approaches where digits are simply
recognized in isolation, the novelty of our approach lies in the manner
in which recognitions scores along with domain specific knowledge about
the frequency distribution of various combination of digits are all
integrated into one unified model. The domain knowledge is derived from
postal directory files. This data feeds into the models as n-grams
statistics that are seamlessly integrated with recognition scores of
digit images. We present the recognition accuracy (90%) achieved on a
set of 20,000 ZIP codes
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