This paper presents a two-level based character recognition method in which a dynamically selection of the most promising zoning scheme for feature extraction allows us to obtain interesting results for character recognition. The first level consists of a conventional neural network and a look-up-table that is used to suggest the best zoning scheme for a given unknown character. The information provided by the first level drives the second level in the selection of the appropriate feature extraction method and the corresponding class-modular neural network. The experimental protocol has shown significant recognition rates for handwritten characters (from 80.82% to 88.13%). © 2011 Springer-Verlag.
CITATION STYLE
Hirabara, L. Y., Aires, S. B. K., Freitas, C. O. A., Britto, A. S., & Sabourin, R. (2011). Dynamic zoning selection for handwritten character recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7042 LNCS, pp. 507–514). https://doi.org/10.1007/978-3-642-25085-9_60
Mendeley helps you to discover research relevant for your work.