Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition

In this paper, we present a new incremental learning strategy for handwritten character recognition systems.This learning strategy enables the recognition system to learn ldquorapidlyrdquo any new character from very few examples.The presented strategy is driven by a confusion detection mechanism in order to control the learning process. Artificial characters generation techniques are used to overcome the problem of lack of learning data when introducing a new character from unseen class. The results show that a good recognition rate (about 90%) is achieved after only 5 learning examples. Moreover, the rate quickly rises to 94% after 10 examples, and approximately 97% after 30 examples. A reduction of error of 40% is obtained by using the artificial characters generation techniques.