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Document Image Decoding Using Markov Source Models
June 1994 (vol. 16 no. 6)
pp. 602-617

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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Index Terms:
document image processing; hidden Markov models; dynamic programming; image coding; document image decoding; Markov source models; communication theory; document image recognition; stochastic finite state automaton; message source; 1D message string; 2D bitmap; decoder; channel models; Viterbi-like dynamic programming; finite state model
Citation:
G.E. Kopec, P.A. Chou, "Document Image Decoding Using Markov Source Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 602-617, June 1994, doi:10.1109/34.295905
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