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 “rapidly” 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.

• Type: Conference
• Year: 2009
• Where: ICDAR
• Authors: Abdullah Almaksour, Éric Anquetil
• Keywords: Artificial Characters Generation, Characters Generation Techniques, Document Analysis, ICDAR 2009, Learning Strategy