Postprocessing of recognized strings using nonstationary Markovianmodels
Bouchaffra, D.
Govindaraju, V.
Srihari, S.N.
Dept. of Comput. Sci., State Univ. of New York, Buffalo, NY;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Oct 1999
Volume: 21,
Issue: 10
On page(s): 990-999
ISSN: 0162-8828
References Cited: 25
CODEN: ITPIDJ
INSPEC Accession Number: 6406667
Digital Object Identifier: 10.1109/34.799906
Current Version Published: 2002-08-06
Abstract
This paper presents nonstationary Markovian models and their
application to recognition of strings of tokens. Domain specific
knowledge is brought to bear on the application of recognizing zip codes
in the US mailstream by the use of postal directory files. These files
provide a wealth of information on the delivery points (mailstops)
corresponding to each zip code. This data feeds into the models as
n-grams, statistics that are integrated with recognition scores of digit
images. An especially interesting facet of the model is its ability to
excite and inhibit certain positions in the n-grams leading to the
familiar area of Markov random fields. We empirically illustrate the
success of Markovian modeling in postprocessing applications of string
recognition. We present the recognition accuracy of the different models
on a set of 20000 zip codes. The performance is superior to the present
system which ignores all contextual information and simply relies on the
recognition scores of the digit recognizers
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