Recognition of unconstrained handwritten numerals by doubly self-organizing neural network
Sung-Bae Cho · 1996
In this paper we present an efficient pattern recognizer based on a self-organizing neural network which can adapt its structure as well as its weights. The network, called doubly self-organizing neural network (DSNN), makes use of the structure-adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundaries as close to the class boundaries as possible. In order to verify the superiority of the DSNN, experiments with the unconstrained handwritten numeral database of Concordia University in Canada were conducted. The proposed method has produced 96.05% of the recognition rate, which we show better than those of several previous methods reported in the literature on the same database.