Document image decoding using Markov source models

Authors:
Gary E. Kopec;Phil A. Chou
Affiliations:
Xerox Palo Alto Research Center, Palo Alto, CA;Xerox Palo Alto Research Center, Palo Alto, CA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Year:
1993

Citing 2
Cited 0

Recognition of handwritten word: first and second order hidden Markov model based approach

Pattern Recognition
Document Image Decoding Using Markov Source Models

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

This paper describes a communication theory approach to document image recognition, pattemed after the use of hidden Markov models in speech recognition. 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 which combines a message source with an imager. The message source produces a string of symbols which contains the information to be transmitted. The imager is modeled as a finite-state transducer which converts the message into an ideal bitmap. The channel transforms the ideal image into a noisy observed image. The decoder estimates the message from the observed-image by finding the aposteriori most probablepath through the combined source and channel models using a Viterbi-like algorithm. Application of the proposed method to decoding telephone yellow pages is described.