Fast Online Incremental Learning with Few Examples For Online Handwritten Character Recognition.

Abdullah Almousa Almaksour (1) , Harold Mouchère (1) , Eric Anquetil (1)

1 IMADOC - Interprétation et Reconnaissance d’Images et de Documents

An incremental learning strategy for handwritten character recognition is proposed in this paper. The strategy is online and fast, in the sense that any new character class can be instantly learned by the system. The proposed strategy aims at overcoming the problem of lack of training data when introducing a new character class. Synthetic handwritten characters generation is used for this purpose. Our approach uses a Fuzzy Inference System (FIS) as a classifier. Results have shown that a good recognition rate (about 90%) can be achieved using only 3 training examples. And such rate rapidly improves reaching 96% for 10 examples, and 97% for 30 ones.

Domaines: Apprentissage [cs.LG]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Abdullah Almousa Almaksour, Harold Mouchère, Eric Anquetil. Fast Online Incremental Learning with Few Examples For Online Handwritten Character Recognition.. Eleventh International Conference on Frontiers in Handwriting Recognition (ICFHR'08), Aug 2008, Canada. pp.623-628. ⟨hal-00491338⟩