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Proceedings Paper

Document image decoding using Markov source models
Author(s): Gary E. Kopec; Philip A. Chou
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Paper Abstract

This paper describes a communication theory approach to document image recognition, patterned after the use of hidden Markov models in speech recognition. In general, 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 automation) which combines a message source with an imager. The message source produces a string of symbols, or text, which contains the information to be transmitted. The imager is modeled as a finite-state transducer which converts the one-dimensional message string into an ideal two-dimensional 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 has been applied to 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. Overall, 99.5% of the listings were correctly recognized, with character classification rates of 98% and 99.6%, respectively, for the names and numbers.

Paper Details

Date Published: 14 April 1993
PDF: 12 pages
Proc. SPIE 1906, Character Recognition Technologies, (14 April 1993); doi: 10.1117/12.143614
Show Author Affiliations
Gary E. Kopec, Xerox Palo Alto Research Ctr. (United States)
Philip A. Chou, Xerox Palo Alto Research Ctr. (United States)


Published in SPIE Proceedings Vol. 1906:
Character Recognition Technologies
Donald P. D'Amato, Editor(s)

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