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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. 602617, June, 1994.  
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@article{ 10.1109/34.295905, author = {G.E. Kopec and P.A. Chou}, title = {Document Image Decoding Using Markov Source Models}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {16}, number = {6}, issn = {01628828}, year = {1994}, pages = {602617}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.295905}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Document Image Decoding Using Markov Source Models IS  6 SN  01628828 SP602 EP617 EPD  602617 A1  G.E. Kopec, A1  P.A. Chou, PY  1994 KW  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; Viterbilike dynamic programming; finite state model VL  16 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
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 finitestate 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 finitestate 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 Viterbilike 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 finitestate model for yellow page columns was constructed and used to decode a database of scanned column images containing about 1100 individual listings.
[1] Adobe Systems,Postscript Language Reference Manual, AddisonWesley, Reading, Mass., 1985.
[2] H. Abelson and A. diSessa,Turtle Geometry. Cambridge, MA: MIT Press, 1980.
[3] D. S. Batory, "A model of transactions on physical databases,"ACM Trans. Database Syst., vol. 7, no. 4, pp. 509539, Dec. 1982.
[4] J. C. Anigbogu and A. Belaiïid, "Application of hidden Markov models to multifont text recognition," inProc. Int. Conf. Document Anal. and Recognit., SaintMalo, France, September, 1991, pp. 785793.
[5] H. Baird, "Document image defect models," inProc. IAPR Workshop on Syntactic and Structural Pattern Recognit., Murray Hill, NJ, June 1990.
[6] L. Bahl, F. Jelinek, and R. Mercer, "A maximum likelihood approach to continuous speech recognition,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI5, no. 2, pp. 179190, Mar. 1983.
[7] F. Chen, L. Wilcox, and D. Bloomberg, "Word spotting in scanned images using hidden Markov models," inProc. 1993 IEEE Int. Conf. Acoust., Speech and Signal Processing, Minneapolis, MN, vol. V, Apr. 2730, 1993, pp. 14.
[8] M. Chen, A. Kundu, and J. Zhou, "Offline handwritten word recognition using a hidden Markov model type stochastic network," to appear inIEEE Trans. Pattern Anal. Machine Inell.
[9] P. Chou, "Recognition of equations using a twodimensional stochastic contextfree grammar," presented at theSPIE Visual Commun. Image Processing (IV), Philadelphia, PA, vol. 1199, Nov. 1989, pp. 852863.
[10] C. Goldfarb,The SGML Handbook. Oxford: Oxford Univ. Press, 1991.
[11] J. E. Hopcroft and J. D. Ullman,Introduction to Automata Theory, Languages, and Computation. Reading, MA: AddisonWesley, 1979.
[12] X. Huang, Y. Ariki, and M. Jack,Hidden Markov Models for Speech Recognition. Edinburgh: Edinburgh Univ. Press, 1990.
[13] F. Jelinek, "Continuous speech recognition by statistical methods,"Proc. IEEE, vol. 64, no. 4, pp. 532556, Apr. 1976.
[14] R. Karp, R. Miller, and S. Winograd, "The Organization of Computations for Uniform Recurrence Equations,"J. ACM, Vol. 14, No. 3, 1967, pp. 563590.
[15] G. Kopec and S. Bagley, "Editing Images of Text," inEP90, R. Furuta, Ed. Cambridge: Cambridge Univ. Press, 1990; also rep. P92000150 (ISTL923) Xerox Palo Alto Res. Center, Palo Alto, CA, Nov. 1992.
[16] G. Kopec, "Rowmajor scheduling of image decoders," Rep. P9200061 (EDL925), XEROX Palo Alto Res. Center, Palo Alto, CA, June 1992.
[17] G. Kopec, "Leastsquares font metric estimation from images,"IEEE Trans. Image Processing, vol. 2, no. 4, pp. 510519, Oct. 1993.
[18] L. Lamport,Latex: A Document Preparation System, AddisonWesley, Reading, Mass., 1986.
[19] Pacific Bell,Smart Yellow Pages, Palo Alto, Redwood City and Menlo Park, 1992.
[20] C. H. Papadimitriou and K. Steiglitz,Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs, NJ: PrenticeHall, 1982.
[21] P. Prusinkiewicz and J. Hanan,Lindenmayer Systems, Fractals and Plants(Lecture Notes in Biomathematics), no. 79. Berlin: SpringerVerlag, 1989.
[22] S. K. Rao, "Regular iterative algorithms and their implementations on processor arrays," Ph.D. dissertation, Stanford Univ., Stanford, CA, Oct. 1985.
[23] R. Rubenstein,Digital Typography. Reading: AddisonWesley, 1988.
[24] D. Sankoff and J. Kruskal, Eds.,Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison. Reading, MA: AddisonWesley, 1983.
[25] M. Tomita, "Parsing 2dimensional language," inACM Int. Workshop on Parsing Technol., 1989, pp. 414424.
[26] K. Wong, G. Casey, and F. Wahl, "Document analysis system,"IBM J. Res. Develop., vol. 26, no. 6, pp. 647656, Nov. 1982.
[27] J. Vlontzos and S.Y. Kung, "Hidden Markov models for character recognition," inProc. 1989 IEEE Int. Conf. Acoust., Speech and Signal Processing, Glasgow, Scotland, May 2326, 1989, pp. 17191722.