title: Handwritten digit recognition using two-layer self-organizing maps.
abstract: In this paper, we present a two-layer self-organizing neural network based method for handwritten digit recognition. The network consists of a base layer self-organizing map and a set of corresponding maps in the second layer. The input patterns are partitioned into subspace in the first layer. Patterns in a subspace are led to the second layer and a corresponding map is built according to the first layer performance. In the classification process, each pattern searches for several closest nodes from the base map and then it is classified into a specified class by determining the nearest model of the corresponding maps in the second layer. The new method yielded higher accuracy and faster performance than the ordinary self-organizing neural network.
authors.affiliation: Department of Electrical Engineering, University of Sydney, NSW, Australia.
authors.forename: J
authors.initials: J
authors.lastname: Wu
authors.forename: H
authors.initials: H
authors.lastname: Yan
authors.forename: A
authors.initials: A
authors.lastname: Chalmers
mesh_terms: D006236:Handwriting; D008377:Maps as Topic; D008433:Mathematics; D008959:Models, Neurological; D008962:Models, Theoretical; D016571:Neural Networks, Computer