Integrated segmentation and recognition of handwritten numerals: Comparison of classification algorithms

Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR

Volumn , Issue , 2002, Pages 303-308

  Liu, Cheng Lin ^a   Sako, Hiroshi ^a   Fujisawa, Hiromichi ^a  

^a HITACHI LTD   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER CLASSIFICATION; CLASSIFICATION ALGORITHM; DISCRIMINATIVE LEARNING; HAND-WRITTEN CHARACTERS; INTEGRATED SEGMENTATION AND RECOGNITION; MINIMUM CLASSIFICATION ERROR CRITERIONS; MINIMUM SQUARE ERRORS; QUADRATIC DISCRIMINANTS;

HEURISTIC METHODS; MAXIMUM LIKELIHOOD ESTIMATION;

STATISTICS;

EID: 10044245433     PISSN: 15505235     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IWFHR.2002.1030927     Document Type: Conference Paper

References (18)

1. Y. Saifullah, M.T. Maury, Classification-based segmentation of ZIP codes, IEEE T runs. System, Man, and Cybernetics, 23(5): 1437-1443, 1993.
2. S-W. Lee, S-Y. Kim, Integrated segmentation and recognition of handwritten numerals with cascade neural net w ork JEEE Trans. System, Man, and Cybernetics, 29(2): 285-290, 1999.
3. C-L. Liu, H. Sako, H. Fujisaw a,P erformance equation of pattern classifiers for handwritten character recognition, Int. J. Do cumentA nalysis and R ecogni-tion, 4(3): 191-204, 2002.
4. J. Bromley, J.S. Denker, Improving rejection performance on handwritten digits by training with rubbish, Neur al Computation 5: 367-370, 1993.
5. L. Yaeger, R. Lyon, B. Webb, Effective training of a neural net w orkcharacter classifier for w ord recognition, A dvanc es in Neud Information Processing Systems 9, M. Mozer, M. Jordan and T. Petsche (Eds), MIT Press, 1997.
6. H. Murase, Online recognition of free-format Japanese handwritings, Proc. 9th ICPR pp. 1143-1147, 1988.
7. H. Fujisaw a, Y. Nakano, K. Kurino, Segmentation methods for character recognition: from segmentation to document structure analysis, Proc. IEEE, 80(7): 1079-1092, 1992.
8. D. Wettschereck, T. Dietterich, Improving the performance of radial basis function networks by learning center locations, A dvanes in Neural Information Processing Systems 4, Morgan-Kauffmann, 1992, pp.11331140.
9. TJ. Krefiel, J. Schiirmann, Pattern classification techniques based on function approximation, Handbook of Char acter R ecognition and Bwment Image Analysis, H. Bunke and P.S.P.Wang (Eds.), World Scientific, 1997, pp.49-78.
10. C-L. Liu, M. Nakaga w a, Evaluation of prototype learning algorithms for nearest neighbor classifier in application to handwritten character recognition, Pattern R ecognitiop,34(3): 601-615, 2001.
11. F. Kimura, K. Takashina,S. Tsuruoka, Y. Miyake, Modified quadratic discriminant functions and the application to Chinese character recognition, IEEE Trans. Pattern A nalysis and Machine Intelligenc e 9(1): 149-153, 1987.
12. F. Kimura, M. Shridhar, Handwritten numeral recognition based on multiple algorithms, Pattern Recognition, 24(10): 969-981
13. C-L. Liu, H. Sako, H. Fujisaw a,Learning quadratic discriminant function for handwritten character recognition, Proc. 16th ICPR Canada, 2002.
14. S. T uly ackv, V. Govindaraju, Probabilistic model for segmentation based w ord recognition with lexicon, Proc. 6th ICDAR, Seattle, 2001, pp. 164-167.
15. U. Rainer, An iterative procedure for the polygonal appro ximationof plane closed curv es, Computer Gr aphics and Image Pvcessing, 1, 244-256, 1972.
16. B-H. Juang, S. Katagiri, Discriminative learning for minimization error classification, IEEE Trans. Signal Processing 40(12): 3043-3054, 1992.
17. J.H. Friedman, Regularized discriminant analysis, J. A to. Statist. Ass, 84(405): 165-175, 1989.
18. T.M. Ha, J. Zimmermann, H. Bunke, Off-line handwritten numeral string recognition by combining segmentation-based and segmentation-free methods, Pattern Recognition, 31(3): 257-272, 1998.