Document image decoding using Markov source models

Gary E. Kopec; Philip A. Chou

doi:10.1117/12.143614

14 April 1993 Document image decoding using Markov source models

Gary E. Kopec, Philip A. Chou

Proceedings Volume 1906, Character Recognition Technologies; (1993) https://doi.org/10.1117/12.143614
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

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.

Citation Download Citation

Gary E. Kopec and Philip A. Chou "Document image decoding using Markov source models", Proc. SPIE 1906, Character Recognition Technologies, (14 April 1993); https://doi.org/10.1117/12.143614

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