An Adaptive Modular Neural Network with Application to Unconstrained Character Recognition

The topology and the capacity of a traditional multilayer neural system, as measured by the number of connections in the network, has suprisingly little impact on its generalization ability. This paper presents a new adaptive modular network that offers superior generalization capability. The new network provides significant fault tolerance, quick adaption to novel inputs, and high recognition accuracy. We utilize this paradigm for recognition of unconstrained handwritten characters.