Offline cursive character challenge: A new benchmark for machine learning and pattern recognition algorithms

Proceedings - International Conference on Pattern Recognition

Volumn 2, Issue , 2006, Pages 913-916

  Camastra, Francesco ^a   Spinetti, Marco ^b   Vinciarelli, Alessandro ^c,d  

^a UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

^b Elbahome   (Italy)

^c IDIAP RESEARCH INSTITUTE   (Switzerland)

^d EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATABASE SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; WORD PROCESSING;

CURSIVE CHARACTER RECOGNITION; PATTERN RECOGNITION ALGORITHMS;

CHARACTER RECOGNITION;

EID: 34047203494     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.895     Document Type: Conference Paper

References (15)

1. C. Bishop. Neural Networks for Pattern Recognition. Cambridge University Press, Cambridge, UK, 1995.
2. R. Bozinovic and S. Srihari. Off-line cursive script word recognition. IEEE Trans. on Patt. Anal. and Mach. Int., 11(1):69-83, January 1989.
3. F. Camastra and A. Vinciarelli. Cursive character recognition by Learning Vector Quantization. Patt. Rec. Lett., 22(6):625-629, May 2001.
4. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:1-25, 1995.
5. P. Gader, M. Mohamed, and J.-H. Chiang. Handwritten word recognition with character and inter-character neural networks. IEEE Trans. on Syst., Man and Cyb.-PartB: Cybernetics, 27:158-164, January 1997.
6. T. Joachim. Making large-scale svm learning practical. In Advances in Kernel Methods- Support Vector Learning, pages 169-184. MIT Press, 1999.
7. th Intl. Conf. on Document Analysis and Recognition, pages 866-869. IEEE Press, 1997.
8. T. Kohonen. Learning vector quantization. In The Handbook of Brain Theory and Neural Networks, pages 537-540. MIT Press, 1995.
9. T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, and K. Torkkola. Lvq-pak: The learning vector quantization program package. Technical Report A30, Helsinki University of Technology, Laboratory of Computer and Information Science, 1996.
10. T. Martinetz, S. Berkovich, and K. Schulten. "Neural Gas" network for vector quantization and its application to time-series prediction. IEEE Trans. on Neural Networks, 4(4):558-569, 1993.
11. th Intl. Conf. on Document Analysis and Recognition, pages 729-732. IEEE Press, 1999.
12. B. Scholkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge (USA), 2002.
13. S. Singh and M. Hewitt. Cursive digit and character recognition on cedar database. In Proc. of Intl. Conf. on Pattern Recognition, pages 569-572. IEEE Press, 2000.
14. M. Stone. Cross-validatory choice and assessment of statistical prediction. Journal of the Royal Stat. Soc., 36(1):111-147, 1974.
15. B. Verma, M. Blumenstein, and M. Ghosh. A novel approach for structural feature extraction: contour vs. direction. Patt. Rec. Lett., 25(9):975-988, 2004.