Handwritten character recognition with the Athena model
A. Abrahamsson, Cris Koutsougeras, C. Papachristou · 2002
The results of using the Athena neural network model in automatic recognition of handwritten English letters are presented. This model has several layers. The training patterns need to be presented once to each layer of neurons, in parallel. The model forms a binary tree of neurons, and the training is done from the root towards the leaves, processing each level in parallel. The learning time is shortened significantly compared to the network of D.E. Rumelhart et al. (1986). The net structure is dynamically decided during the learning process. The goal is to enhance the adaptive learning capability to recognize more and more patterns. In the approach presented, an incremental learning scheme based on the Athena neural net is introduced. The objective is to learn misclassified patterns adaptively by dynamically correcting the neural net topological structure and the individual neuron's weights and threshold. Incremental learning is performed without losing information about previously learned patterns. The experiments conducted on the recognition of handwritten English letters utilizing Athena and the incremental learning scheme show a significant recognition success rate.>