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

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2 Author(s)
Abdullah Almaksour ; INSA de Rennes, Rennes, Germany ; Eric Anquetil

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.

Published in:

2009 10th International Conference on Document Analysis and Recognition

Date of Conference:

26-29 July 2009