This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1994
Volume: 16,
Issue: 6
On page(s): 602-617
ISSN: 0162-8828
References Cited: 27
CODEN: ITPIDJ
INSPEC Accession Number: 4722517
Digital Object Identifier: 10.1109/34.295905
Current Version Published: 2002-08-06
Abstract
Document image decoding (DID) is a communication theory approach
to document image recognition. In DID, a document recognition problem is
viewed as consisting of three elements: an image generator, a noisy
channel and an image decoder. A document image generator is a Markov
source (stochastic finite-state automaton) that combines a message
source with an imager. The message source produces a string of symbols,
or text, that contains the information to be transmitted. The imager is
modeled as a finite-state transducer that converts the 1D message string
into an ideal 2D bitmap. The channel transforms the ideal image into a
noisy observed image. The decoder estimates the message, given the
observed image, by finding the a posteriori most probable path through
the combined source and channel models using a Viterbi-like dynamic
programming algorithm. The proposed approach is illustrated on the
problem of decoding scanned telephone yellow pages to extract names and
numbers from the listings. A finite-state model for yellow page columns
was constructed and used to decode a database of scanned column images
containing about 1100 individual listings
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