A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

Pattern Recognition

Volumn 40, Issue 3, 2007, Pages 898-919

  Sadri, Javad ^a   Suen, Ching Y ^a   Bui, Tien D ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

Contextual knowledge; Genetic algorithm; Handwritten digit recognition; Handwritten numeral string recognition; Handwritten numeral string segmentation

Indexed keywords

DATABASE SYSTEMS; EVALUATION; IMAGE SEGMENTATION; KNOWLEDGE ACQUISITION; NEURAL NETWORKS; NUMERICAL ANALYSIS; PATTERN RECOGNITION;

CONTEXTUAL KNOWLEDGE; HANDWRITTEN DIGIT RECOGNITION; HANDWRITTEN NUMERAL STRING RECOGNITION; HANDWRITTEN NUMERAL STRING SEGMENTATION;

GENETIC ALGORITHMS;

EID: 33750532285     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.08.002     Document Type: Article

References (32)

1. Pal U., Belaid A., and Choisy Ch. Touching numeral segmentation using water reservoir concept. Pattern Recognition Lett. 24 (2003) 261-272
2. Oliveira L.S., Sabourin R., Bortolozzi F., and Suen C.Y. Automatic segmentation of handwritten numerical strings: a recognition and verification strategy. IEEE Trans. Pattern Anal. Mach. Intell. 24 11 (2002) 1438-1454
3. L.S. Oliveira, R. Sabourin, Support vector machines for handwritten numerical string recognition, Proceedings of the International Workshop on Frontiers in Handwriting Recognition (IWFHR-9), Tokyo, Japan, 2004, pp. 39-44.
4. Liu C.L., Sako H., and Fujisawa H. Effects of classifier structure and training regimes on integrated segmentation and recognition of handwritten numeral strings. IEEE Trans. Pattern Recognition Mach. Intell. 26 11 (2004) 1395-1407
5. J. Sadri, C.Y. Suen, T.D. Bui, New approach for segmentation and recognition of handwritten numeral strings, document recognition and retrieval XII, Proceedings of SPIE-IS&T Electronic Imaging, SPIE, vol. 5767, 2005, pp. 92-100.
6. N.W. Strathy, C.Y. Suen, A. Krzyzak, Segmentation of handwritten digits using contour features, in: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR93), 1993, pp. 577-580.
7. Kim K.K., Kim J.H., and Suen C.Y. Segmentation-based recognition of handwritten touching pairs of digits using structural features. Pattern Recognition Lett. 23 (2002) 13-24
8. Casey R.G., and Lecolinet E. A survey of methods and strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18 7 (1996) 690-706
9. Chi Z., Suters M., and Yan H. Separation of single and double touching handwritten numeral strings. Opt. Eng. 34 (1995) 1159-1165
10. Z. Shi, V. Govindaraju, Segmentation and recognition of connected handwritten numeral strings, in: Progress in Handwritten Recognition, World Scientific, Singapore, 1996, pp. 515-518.
11. M. Cheriet, Y.S. Huang, C.Y. Suen, Background region based algorithm for the segmentation of connected digits, in: Proceedings of the International Conference on Pattern Recognition (ICPR92), 1992, pp. 619-622.
12. Lu Z., Chi Z., Siu W., and Shi P. A background-thinning-based approach for separating and recognizing connected handwriting digit strings. Pattern Recognition 32 (1999) 921-933
13. Chen Y.K., and Wang J.F. Segmentation of single or multiple touching handwritten numeral strings using background and foreground analysis. IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000) 1304-1317
14. S. Choi, I. Oh, A segmentation-free recognition of two touching numerals using neural networks. in: Proceedings of 5th ICDAR (ICDAR'99), Bangalore, India, 1999, pp. 253-256.
15. J. Sadri, C.Y. Suen, T.D. Bui, Automatic segmentation of unconstrained handwritten numeral strings, in: Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR 2004), Tokyo, Japan, October 2004, pp. 317-322.
16. Shridhar M., and Badrelin A. Recognition of isolated and simply connected handwritten numerals. Pattern Recognition 19 1 (1986) 1-12
17. H. Nishida, S. Mori, A model-based split-and-merge method for character string recognition, in: P.S.P. Wang, (Ed.), Document Image Analysis, World Scientific, Singapore, 1994, pp. 209-226.
18. O. Matan, C.J.C. Burges, Recognition of overlapping hand-printed characters by centered-objects integrated segmentation and recognition, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN'91), 1991, pp. 504-511.
19. L.S. Oliveira, E. Lethelier, F. Bortolozzi, R. Sabourin, A new approach to segment handwritten digits, Seventh International Workshop on Frontiers in Handwritten Recognition (IWFHR-7), Amsterdam, September 2000, pp. 577-582.
20. X. Wang, V. Govindaraju, S. Srihari, Holistic recognition of touching digits, in: The Proceedings of Sixth International Workshop on Frontiers in Handwriting Recognition (IWFHR'98), 1998, pp. 295-303.
21. T. Yamaguchi, Y. Nakano, M. Maruyama, H. Miyao, T. Hananoi, Digit classification on signboards for telephone number recognition, Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2003, pp. 359-363.
22. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley, Longman Inc., Reading, MA, New York
23. Koza J.R. Genetic Programming-On The Programming of Computers by Means of Natural Selection (1992), The MIT Press, Cambridge
24. Zhang T.Y., and Suen C.Y. A fast parallel algorithm for thinning digital patterns. Commun. ACM 27 3 (1984) 236-239
25. Liu C.L., Nakashima K., Sako H., and Fujisawa H. Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognition 36 (2003) 2271-2285
26. Dong J., Krzyzak A., and Suen C.Y. Fast SVM training algorithm with decomposition on very large training sets. IEEE Trans. Pattern Anal. Mach. Intell. 27 4 (2005) 603-618
27. Liu C.L., Nakashima K., Sako H., and Fujisawa H. Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognition 37 (2004) 265-279
28. Bishop C.M. Neural Networks for Pattern Recognition (2003), Oxford University Press, Oxford
29. G. Wahba, X. Lin, F. Gao, D. Xiang, R. Klein, B. Klein, The bias-variance trade-off and the randomized GACV, Proceedings of the 13th Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, 2001, pp. 8-31.
30. Z. Shi, S.N. Srihari, Y.-C. Shin, V. Ramanaprasad, A system for segmentation and recognition of totally unconstrained handwritten numeral strings, Proceedings of the International Conference on Document Analysis and Recognition (ICDAR97), vol. 2, 1997, pp. 455-458.
31. C.L. Liu, H. Sako, H. Fujisawa, Integrated segmentation and recognition of handwritten numerals: comparison of classification algorithms, Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02), Ontario, Canada, 2002, pp. 303-308.
32. Britto A.S., Sabourine R., Bortolozzi F., and Suen C.Y. Recognition of handwritten numeral strings using a two-stage HMM-based method. Int. J. Doc. Anal. Recognition 5 2-3 (2003) 102-117