Handwritten alpha-numeric recognition by a self-growing neural network 'CombNET-II'
Akira Iwata, Yoshihisa Suwa, Yutaka INO, Nobuo Suzumura · 2003
CombNET-II is a self-growing four-layer neural network model which has a comb structure. The first layer constitutes a stem network which quantizes an input feature vector space into several subspaces and the following 2-4 layers constitute branch network modules which classify input data in each sub-space into specified categories. CombNET-II uses a self-growing neural network learning procedure, for training the stem network. Back propagation is utilized to train branch networks. Each branch module, which is a three-layer hierarchical network, has a restricted number of output neurons and inter-connections so that it is easy to train. Therefore CombNET-II does not cause the local minimum state since the complexities of the problems to be solved for each branch module are restricted by the stem network. CombNET-II correctly classified 99.0% of previously unseen handwritten alpha-numeric characters.>