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Communication Dans Un Congrès Année : 2007

A progressive learning method for symbols recognition

Résumé

This paper deals with a progressive learning method for symbols recognition which improves its own recognition rate when new symbols are recognized in graphics documents. We propose a discriminant analysis method which provides allocation rules from learning samples with known classes. However a discriminant analysis method is efficient only if learning samples and data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each observation to take into account the parasitic effects between the data and the learning samples. We propose also an adaptation to consider the user feedback.
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Dates et versions

inria-00118309 , version 1 (04-12-2006)

Identifiants

  • HAL Id : inria-00118309 , version 1

Citer

Sabine Barrat, Salvatore Tabbone. A progressive learning method for symbols recognition. 22nd Annual ACM Symposium on Applied Computing - SAC 2007, Mar 2007, Seoul, South Korea. pp.627-631. ⟨inria-00118309⟩
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