Evidential combination of multiple HMM classifiers for multi-script handwritting recognition

Yousri Kessentini (1) , Thomas Burger (2) , Thierry Paquet (1)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192)

In this work, we focus on an improvement of a multi-script handwritting recognition system using a HMM based classifiers combination. The improvement relies on the use of Dempster-Shafer theory to combine in a finer way the probabilistic outputs of the HMM classifiers. The experiments are conducted on two public databases written on two different scripts : IFN/ENIT (latin script) and RIMES (arabic script). The obtained results are compared with the classical algorithms of the field and the superiority of the proposed approach is shown.

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

Yousri Kessentini, Thomas Burger, Thierry Paquet. Evidential combination of multiple HMM classifiers for multi-script handwritting recognition. IPMU 2010, Jun 2010, Dortmund, Germany. ⟨10.1007/978-3-642-14049-5_46⟩. ⟨hal-00476863⟩