Article Dans Une Revue Pattern Recognition Année : 2006

Learning Stochastic Edit Distance: application in handwritten character recognition

Résumé

Many pattern recognition algorithms are based on the nearest neighbour search and use the well known edit distance, for which the primitive edit costs are usually fixed in advance. In this article, we aim at learning an unbiased stochastic edit distance in the form of a finite-state transducer from a corpus of (input,output) pairs of strings. Contrary to the other standard methods, which generally use the Expectation Maximisation algorithm, our algorithm learns a transducer independently on the marginal probability distribution of the input strings. Such an unbiased way to proceed requires to optimise the parameters of a conditional transducer instead of a joint one. We apply our new model in the context of handwritten digit recognition. We show, carrying out a large series of experiments, that it always outperforms the standard edit distance.

Fichier principal
Vignette du fichier
Oncina_Sebban_PR06.pdf (224.21 Ko) Télécharger le fichier
Loading...

Dates et versions

hal-00114106 , version 1 (15-11-2006)

Licence

Identifiants

Citer

Jose Oncina, Marc Sebban. Learning Stochastic Edit Distance: application in handwritten character recognition. Pattern Recognition, 2006, 39 (9), pp.1575-1587. ⟨10.1016/j.patcog.2006.03.011⟩. ⟨hal-00114106⟩
259 Consultations
658 Téléchargements

Altmetric

Partager

  • More