Reconocimiento de caracteres manuscritos mediante información del proceso inverso en la realización de su trazo
DOI:
https://doi.org/10.17533/udea.redin.15971Keywords:
Reconocimiento de caracteres, RNA, SVM, GMMAbstract
En este trabajo se describe una metodología para el reconocimiento de caracteres manuscritos mediante información del trazo en orden inverso descrito a través de ondículas. La información para la reconstrucción y reconocimiento del trazo se hace mediante la extracción de los llamados nodos óptimos. En este trabajo se utilizaron 20 nodos. Como función de aproximación se usó la función spline natural llamada slalom. Los experimentos se realizaron con tres clasificadores: una red neuronal, una máquina de vector soporte y un modelo de mezclas Gaussianas. El sistema se evaluó con una base de datos de siete escritores con 50 trazos por cada carácter del alfabeto inglés. La tasa de reconocimiento global al usar los tres clasificadores oscila entre 98 y 98,7 %.Downloads
References
M. Morita, R. Sabourin, F. Bortolozzi, C. Y. Suen. “A Recognition and Verification Strategy for Handwritten Word Recognition”. Proceedings ICDAR’03. Edinburgh-Scotland. 2003. pp. 482-486.
J. Mantas. “An Overview of Character Recognition Methodologies”. Pattern Recognition. Vol. 19. 1986. pp. 425-430.
F. Nouboud, Plamondon. “On-Line Recognition of Handprinted Chara.cters: Survey and Beta Tests”. Pattern Recognition. Vol. 23. 1990. pp. 1031-1044.
P. Rejean, N. Sargur, N. Srihari. “On-Line and Off- Line Handwriting Recognition: A Comprehensive Survey”. 1EEE Transactions on PAMI. Vol. 22. 2000. 63-84.
A. Lemieux, C. Gagne, M. Parizeau. “Genetical Engineering of Handwriting Representations”. Proc. of the International Workshop on Frontiers in Handwriting Recognition (IWFHR).Ontario (Canadá). 2002. pp. 145-150.
H. Mitoma, S. Uchida, H. Sakoe. “Online character recognition based on elastic matching and quadratic discrimination”. Proceedings of 8th International Conference on Document Analysis and Recognition. Vol. 1. 2005. pp. 36-40.
L. Koerich. Large Vocabulary Off-Line Handwritten Word Recognition. PhD thesis, École de Technologie Supérieure, Montreal-Canada. 2002. pp. 17-34.
F. Bortolozzi, A. Souza, L. S. Oliveira, M. Morita, Recent Advances in Handwritten Recognition, Document analysis. U. Pal, S. K. Parui, B. B. Chaudhuri (editors). Montreal. 2005. pp. 1-31.
A. M. Namboodiri, A. K. Jain. “On-line Handwritten Script Recognition”. IEEE Trans. PAMI. Vol. 26. 2004. pp. 124-130.
A. L. Koerich, R. Sabourin, C.Y. Suen. Large vocabulary off-line handwriting recognition: A survey. Pattern Anal Applic. Vol. 6. 2003. pp. 97-121.
L. Cheng-Lin, S. Jaeger, M. Nakagawa. Online Recognition of Chinese Characters: The State-of-the- Art”. IEEE Trans. on Pattern Analysis and Machine Intelligence. Vol. 26. 2004. pp.198-203.
Y. Kato, M. Yasuhara. “Recovery of Drawing Order from single-Stroke Handwriting Images”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 22. 2000. pp. 938-949.
N. Mezghani, A. Mitiche, M. Cheriet. “On-Line Recognition of Handwritten Arabic Characters using a Kohonen Network”. Proc. of the 8th Int, Workshop on Frontiers in Handwriting Recognition. Ontario (Canadá). 2002. pp. 490-495.
M. Yokobayashi, T.Wakahara. “Segmentation and recognition of characters in scene images using selective binarization in color space and gat correlation”. Eighth International Conference on Document Analysis and Recognition ICDAR’05. Seoul. Vol. 1. 2005. pp.167-171.
M. Yokobayashi,T. Wakahara. “Binarization and recognition of degraded characters using a maximum separability axis in color space and gat correlation”. 18th International Conference on Pattern Recognition ICPR 2006. Hong Kong. Vol. 2. 2006. pp. 885-888.
K. Toscano, G. Sánchez, M. Nakano, H. Pérez, M. Yasuhara. “Cursive Character Recognition System“. CERMA 2006. Cuernavaca. Vol. II. 2006. pp. 62-67.
C. Burges B. Schölkopf, A. Smola. Advances in kernel methods: Support vector machines. Cambridge, MA: MIT Press. 1999. pp.327-352.
C. Burges. “A tutorial on support vector machines for pattern recognition”. Data Mining and Knowledge Discovery. Kluwer Academic Publishers. Boston. Vol. 2. 1998. pp. 121-167.
V. Ñ. Vapnik. The nature of statistical learning theory. New York: Springer-Verlag, 1995. pp. 138-216.
D. Reynolds, R. C. Rose. “Robust Text-Independent Speaker Idetification Using Gaussian Mixture Speaker Models”. IEEE Trans. Speech and audio Proc. Vol. 3. 1995. pp. 72-83.
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