Handwriting recognition using local methods for global recognition

Christophe Choisy 1 Abdel Belaïd 1
1 READ - READ
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : These last years, many systems for handwriting recognition were developped. Because of the variability of writing the elastic approachs such Hidden Markov Models, DTW, Markov Random Fields, that can deal with local deformations become very popular. These approach are based on local observations which ones are given by features extraction, and more recently by global models as Neural Networks. SVM and NN are efficient to recognize patterns with small deformations. In the case of words recognition the major difficulty is to deal with the length variability of images. A Recurrent NN was used [cite SENIOR] to overcome this problem but deals with a local context, loosing a part of the power of NN, and gives some difficults to label the frames in the learning step.
Type de document :
Communication dans un congrès
International Conference on Document Analysis and Recognition, 2001, Seattle, USA, 5 p, 2001
Liste complète des métadonnées

https://hal.inria.fr/inria-00100457
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:45:56
Dernière modification le : jeudi 11 janvier 2018 - 06:19:59

Identifiants

  • HAL Id : inria-00100457, version 1

Collections

Citation

Christophe Choisy, Abdel Belaïd. Handwriting recognition using local methods for global recognition. International Conference on Document Analysis and Recognition, 2001, Seattle, USA, 5 p, 2001. 〈inria-00100457〉

Partager

Métriques

Consultations de la notice

108