Off-line Handwritten Word Recognition Using a Mixed HMM-MRF Approach

George Saon 1 Abdel Belaïd 1
1 READ - READ
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper we present a two-dimensional stochastic method for the recognition of unconstrained handwritten words in a small lexicon. The method is based on an efficient combination of hidden Markov models ({\sc hmm}s) and causal Markov random fields ({\sc mrf}s). It operates in a holistic manner, at the pixel level, on scaled binary word images which are assumed to be random field realizations. The state-related random fields act as smooth local estimators of specific writing strokes by merging conditional pixel probabilities along the columns of the image. The {\sc hmm} component of our model provides an optimal switching mechanism between sets of {\sc mrf} distributions in order to dynamically adapt to the features encountered during the left-to-right image scan. Experiments performed on a French omni-scriptor, omni-bank database of handwritten legal check amounts provided by the A2iA company are described in great extent.
Type de document :
Communication dans un congrès
4th International Conference on Document Analysis and Recognition - ICDAR'97, Aug 1997, Ulm, Germany. IEEE Computer Society, 1, pp.118 - 122, 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition. 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=619825〉. 〈10.1109/ICDAR.1997.619825〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00537568
Contributeur : Abdel Belaid <>
Soumis le : jeudi 18 novembre 2010 - 16:16:51
Dernière modification le : mardi 24 avril 2018 - 13:37:27

Identifiants

Collections

Citation

George Saon, Abdel Belaïd. Off-line Handwritten Word Recognition Using a Mixed HMM-MRF Approach. 4th International Conference on Document Analysis and Recognition - ICDAR'97, Aug 1997, Ulm, Germany. IEEE Computer Society, 1, pp.118 - 122, 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition. 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=619825〉. 〈10.1109/ICDAR.1997.619825〉. 〈inria-00537568〉

Partager

Métriques

Consultations de la notice

125