A Practical Neural Network for Handwritten Character Recognition Based on dynamics-Based Active Learning and Self-Organization of Feedback
Naotake Natori, Kazuo Nishimura · IEEJ Transactions on Electronics Information and Systems · 1996
This paper proposes a novel neural network which realizes practical recognition ability for handwritten characters by modeling human active learning and recognition. The neural network incorporates a human handwriting model for active learning and a self-organizing feedback mechanism for active recognition. Emphasis is placed on the feedback mechanism which imitates human selective attention to a particular portion of a character. The result of the first rough classification by a structured neural network is fed back to the early processing of an input character. This feedback mechanism is self-organized by using neural network technology. The recognition ability of the proposed neural network has been evaluated for actual field data. The evaluation results have shown that the self-organizing feedback mechanism considerably improves the recognition ability and that the combination of the human handwriting model and the feedback mechanism leads to a practical level of recognition ability.