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

Abdullah Almousa Almaksour 1 Eric Anquetil 1
1 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : 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.
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Submitted on : Friday, June 11, 2010 - 11:41:40 AM
Last modification on : Thursday, January 7, 2021 - 4:18:42 PM
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  • HAL Id : hal-00491335, version 1


Abdullah Almousa Almaksour, Eric Anquetil. Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition. Tenth International Conference on Document Analysis and Recognition (ICDAR2009), Jul 2009, Spain. pp.81-85. ⟨hal-00491335⟩



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