Using Consensus Sequence Voting to Correct OCR Errors

Computer Vision and Image Understanding

Volumn 67, Issue 1, 1997, Pages 39-47

  Lopresti, Daniel ^a   Zhou, Jiangying ^a  

^a Panasonic Corporation of North America   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR CORRECTION; HEURISTIC METHODS; SCANNING;

CONSENSUS SEQUENCE VOTING PROCEDURE;

OPTICAL CHARACTER RECOGNITION;

EID: 0031185398     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1006/cviu.1996.0502     Document Type: Article

References (21)

1. C. Nadal, R. Legault, and C. Y. Suen, Complementary algorithms for the recognition of totally unconstrained handwritten numerals, in Proceedings, International Conference on Pattern Recognition, Atlantic City, NJ, June 1990, Vol. 1, pp. 443-449.
2. M. Sabourin, A. Mitiche, D. Thomas, and G. Nagy, Classifier combination for hand-printed digit recognition, in Proceedings, Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, October 1993, pp. 163-166.
3. T. K. Ho, Recognition of handwritten digits by combining independent learning vector quantizations, in Proceedings, Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, October 1993, pp. 818-821.
4. T. K. Ho, J. J. Hull, and S. N. Srihari, On multiple classifier systems for pattern recognition, in Proceedings, 11th International Conference on Pattern Recognition, The Netherlands, September 1992, pp. 84-87.
5. E. Mandler and J. Schurmann, Combining the classification results of independent classifiers based on Dempster-Shafer theory of evidence, in Pattern Recognition and Artificial Intelligence (E. S. Felsema and L. N. Kanai, Eds.), Elsevier Science, North-Holland, Amsterdam, 1988.
6. Y. S. Huang and C. Y. Suen, Combination of multiple classifiers with measurement values, in Proceedings, Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, October 1993, pp. 598-601.
7. T. K. Ho, J. J. Hull, and S. N. Srihari, Word recognition with multi-level contextual knowledge, in Proceedings, First International Conference on Document Analysis and Recognition, St. Malo, France, October 1991, pp. 905-915.
8. B. Plessis, A. Sicsu, L. Heutte, E. Menu, E. Lecolinet, O. Debon, and J. V. Moreau, A multi-classifier combination strategy for the recognition of handwritten cursive words, in Proceedings, Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, October 1993, pp. 642-645.
9. R. M. K. Sinha and B. Prasada, Visual text recognition through contextual processing, Pattern Recognit. 21(5), 1988.
10. S. V. Rice, J. Kanai, and T. A. Nartker, A difference algorithm for OCR-generated text, in Proceedings, IAPR Workshop on Structural and Syntactic Pattern Recognition, Bern, Switzerland, August 1992.
11. V. P. Concepcion and D. P. D'Amoto, Synchronous tracking of outputs from multiple OCR systems, SPIE Character Recognit. Technol., 1906, 1993.
12. J. C. Handley and T. B. Hickey, Merging optical character recognition outputs for improved accuracy, in Proceedings, RIAO Conference on Intelligent Text and Image Handling, Barcelona, Spain, April 1991, pp. 160-174.
13. C. H. Chen and J. L. DeCurtins, Word recognition in a segmentation-free approach to OCR, in Proceedings, Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, October 1993, pp. 573-576.
14. R. A. Wagner and M. J. Fischer, The string-to-string correction problem, J. Assoc. Comput. Mach., 21(1), 1974, 168-173.
15. J. Esakov, D. P. Lopresti, and J. S. Sandberg, Classification and distribution of optical character recognition errors, in Proceedings, IS&T/SPIE International Symposium on Electronic Imaging, San Jose, CA, February 1994 (L. M. Vincent and T. Pavlidis, Eds.), Vol. 2181, pp. 204-216.
16. J. Esakov, D. P. Lopresti, J. S. Sandberg, and J. Zhou, Issues in automatic OCR error classification, in Proceedings, Third Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, April 1994, pp. 401-412.
17. J. B. Kruskal, An overview of sequence comparison: Time warps, string edits, and macromolecules, SIAM Rev. 25(2), 1983, 201-237.
18. L. R. Rabiner, A. E. Rosenberg, and S. E. Levinson, Considerations in dynamic time warping algorithms for discrete word recognition, IEEE Trans. Acoust. Speech Signal Process. ASSP-26(6), 1978, 575-582.
19. J. W. Fickett, Fast optimal alignment, Nucleic Acids Res. 12(1), 1984, 175-179.
20. H. Carrillo and D. Lipman, The multiple sequence alignment problem in biology, SIAM J. Appl. Math. 48(5), 1988, 1073-1082.
21. J. Zhou and D. Lopresti, Repeated sampling to improve classifier accuracy, in Proceedings, IAPR Workshop on Machine Vision Applications, Kawasaki, Japan, December 1994, pp. 346-351.