Postprocessing of recognized strings using nonstationary markovian models

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 21, Issue 10, 1999, Pages 990-999

  Bouchaffra, Djamel ^a   Govindaraju, Venu ^a   Srihari, Sargur N ^a  

^a the State University of New York   (United States)

Author keywords

Class conditional probability; Markov random fields; Nonstationary hidden markov models; Postprocessing; Zip code recognition

Indexed keywords

IMAGE ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY; STATISTICS;

BAYESIAN FRAMEWORK; CLASS CONDITIONAL PROBABILITY; IMAGE ANALYSIS; NONSTATIONARY HIDDEN MARKOV MODELS; ZIP CODE RECOGNITION;

CHARACTER RECOGNITION;

EID: 0033208990     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.799906     Document Type: Article

References (25)

1. T.W. Anderson and L.A. Goodman, Statistical Inference about Markov Chains, Ann. Math Statistics, vol. 28, pp. 89-110, 1957.
2. D. Bouchaffra, V. Govindaraju, and S. Srihari, A Methodology for Determining Probability of Correctness of Recognizer Scores, Proc. IEEE CoJ. Computer Vision and Pattern Recognition, Santa Barbara, Calif., June 1998.
3. D. Bouchaffra, E. Koontz, V. Kripasundar, R.K. Srihari, and S.N. Srihari, Integrating Signal and Language Context to Improve Handwritten Phrase Recognition: Alternative Approaches, Proc. Sixth Int'l Workshop Artificial Intelligence and Statistics, Ft. Lauderdale, Fla., Jan. 1997.
4. D. Bouchaffra, E. Koontz, V. Kripasundar, and R.K. Srihari, Incorporating Diverse Information Sources in Handwriting Recognition Postprocessing, Int'l J. Imaging Systems and Technology, vol. 7, no. 4, June 1996.
5. P.P. Brown, V.J. Delia Pietra, P.V. deSouza, J.C. Lai, and R.L. Mercer, Class-Based n-Grams Models of Natural Language, Computational Linguistics, vol. 18, no. 4, pp. 467-479, 1992.
6. R.L. Dobrushin, The Description of Random Fields by Means of Conditional Probabilities and Conditions of Its Regularity, Theory Probability Applications, vol. 13, pp. 197-224, 1968.
7. J. Favata and G. Srikantan, A Multiple Feature/Resolution Approach to Handprinted Digit and Character Recognition, Int'l J. Imaging Systems and Technology, vol. 7, pp. 304-311, 1996.
8. V. Govindaraju, A. Shekhawat, and S. Srihari, Determining Delivery Point Code in US Mailstream, Proc. IWFHR III, 1993.
9. T.M. Ha and H. Bunke, Off-Line Handwritten Numeral String Recognition, internal report, Univ. of Berne, Institut für Informatik und Angewandte Mathematik Neubrückstr, Switzerland, 1997.
10. J.J. Hull, Incorporating Language Syntax in Visual Text Recognition with a Statistical Model, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 12, Dec. 1996.
11. A. Jessop, Informed Assessments. Ellis Horwood, 1990.
12. G. Kim and V. Govindaraju, A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 366-379, 1997.
13. G. Kim and V. Govindaraju, Bankcheck Recognition Using Cross Validation of Courtesy and Legal Amount Fields, Int'l J. Pattern Recognition and Artificial Intelligence, vol. 19, no. 4, pp. 366-379, 1997.
14. F. Kimura and M. Sridhar, Handwritten Numerical Recognition Based on Multiple Algorithms, Pattern Recognition, vol. 24, no. 10, pp. 969-983, 1991.
15. A. Kundu and Y. He, On Optimal Order in Modeling Sequence of Letters in Words in Common Language as a Markov Chain, Pattern Recognition, vol. 24, no. 7, pp. 603-608, 1991.
16. M.Y. Chen, A. Kundu, and J. Zhou, Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, pp. 481-496, 1994.
17. C. Oliver et al., Optimal Order of Markov Models Applied to Bankchecks, Int'l ]. Pattern Recognition and Artificial Intelligence, vol. 19, no. 4, 1997.
18. L.R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.
19. Z. Shi and V. Govindaraju, Segmentation and Recognition of Connected Handwritten Numeral Strings, Pattern Recognition, vol. 30, pp. 1,501-1,504, 1997.
20. R. Shingal and G. Toussaint, Experiments in Text Recognition with the Modified Viterbi Algorithm, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 1, no. 2, 1979.
21. M. Sridhar and A. Badreldin, Context-Directed Segmentation Algorithm for Handwritten Numeral Strings, Image and Vision Computing, vol. 5, no. 1, pp. 3-9, 1997.
22. L. Lam and C.Y. Suen, Structural Classification and Relaxation Matching of Totally Unconstrained Handwritten Zip Code Numbers, Pattern Recognition, vol. 21, no. 19, pp. 19-31, year?
23. C.Y. Suen et al., Computer Recognition of Unconstrained Handwritten Numerals, IEEE Proc., vol. 80, pp. 1,162-1,180, 1992.
24. R. Yee, M. Teo, and R. Shinghal, A Hybrid Classifier for Recognizing Handwritten Numerals, Proc. Fourth Int'l Conf. Document Analysis and Recognition, Ulm, Germany, Aug. 1997.
25. J. Zhou, Q. Gan, and C. Suen, A High Performance Hand-Printed Numeral Recognition System, Proc. Fourth Int'l Conf. Document Analysis and Recognition, Ulm, Germany, Aug. 1997.