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Document Image Decoding by Heuristic Search
September 1996 (vol. 18 no. 9)
pp. 945-950

Abstract—This correspondence describes an approach to reducing the computational cost of document image decoding by viewing it as a heuristic search problem. The kernel of the approach is a modified dynamic programming (DP) algorithm, called the iterated complete path (ICP) algorithm, that is intended for use with separable source models. A set of heuristic functions are presented for decoding formatted text with ICP. Speedups of 3-25 over DP have been observed when decoding text columns and telephone yellow pages using ICP and the proposed heuristics.

[1] F. Chen, D. Bloomberg, and L. Wilcox, "Spotting Phrases in Lines of Imaged Text," Document Recognition II, L. Vincent and H. Baird, eds., Proc. SPIE, vol. 2,422, pp. 256-269, 1995.
[2] G.E. Kopec and P.A. Chou, “Document Image Decoding Using Markov Source Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 602-617, June 1994.
[3] A. Kam, "Heuristic Document Image Decoding Using Separable Markov Models," M.I.T. Master of Science thesis, June 1993.
[4] A. Kam and G. Kopec, "Separable Source Models for Document Image Decoding," Document Recognition II, L. Vincent and H. Baird, eds., Proc. SPIE, vol. 2,422, pp. 84-97, 1995.
[5] S.S. Kuo and O. Agazzi, “Keyword Spotting in Poorly Printed Documents Using Pseudo 2-D Hidden Markov Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 8, pp. 842-848, Aug. 1994.
[6] Pacific Bell, Smart Yellow Pages, Palo Alto, Redwood City, and Menlo Park. 1992.
[7] J. Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, Reading, Mass., 1984.

Index Terms:
Document image decoding, Markov models, heuristic search, dynamic programming.
Citation:
Anthony C. Kam, Gary E. Kopec, "Document Image Decoding by Heuristic Search," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 945-950, Sept. 1996, doi:10.1109/34.537350
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