A Pratical Model to Simulate Human Handwriting and Its Application to Active Learning for Handwritten Character Recognition
Kazuo Hishimura, Naotake Natori · IEEJ Transactions on Electronics Information and Systems · 1996
This paper proposes a practical handwriting model to produce character patterns which resemble those written by a human. The practicability of the model has been examined by handwriting simulation and handwritten character recognition by a neural network built with the model. As a successful application of the model, this paper also proposes a new efficient learning of a neural network for handwritten character recognition. Like human learning, the proposed learning acquires excellent recognition ability for unknown character patterns only from a small number of typical character patterns. The recognition rates exceed those by a conventional statistical method. This application not only provides an effective means for handwritten character recognition but also proves the validity of the proposed handwriting model.