InforLorV4, Main, Exploration, bibRecord, 000932

A PGM-based System for Arabic Handwritten Word Recognition

Identifieur interne : 000932 ( Main/Exploration ); précédent : 000931; suivant : 000933

A PGM-based System for Arabic Handwritten Word Recognition

Auteurs : Afef Kacem [Tunisie] ; Khémiri Akram [Tunisie] ; Belaïd Abdel [France]

Source :

RBID : Hal:hal-01254553

English descriptors

mix :
- feature and image descriptors, statistical pattern recognition, image modelling.

Abstract

This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple and easily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders and diacritic points. The system is evolved based on vertical and horizontal Hidden Markov Models and Dynamic Bayesian Network. Our strategy consists of looking for various architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT database and ancient manuscripts strongly support the feasibility of the proposed system. The recognition rates achieve 91.89% (IFN/ENIT) and 94.61% (ancient manuscripts).

Url:

https://hal.inria.fr/hal-01254553

DOI: 10.5565/rev/elcvia.575

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000373
to stream Hal, to step Curation: 000373
to stream Hal, to step Checkpoint: 000867
to stream Main, to step Merge: 000933
to stream Main, to step Curation: 000932

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple and easily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders and diacritic points. The system is evolved based on vertical and horizontal Hidden Markov Models and Dynamic Bayesian Network. Our strategy consists of looking for various architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT database and ancient manuscripts strongly support the feasibility of the proposed system. The recognition rates achieve 91.89% (IFN/ENIT) and 94.61% (ancient manuscripts).</div>
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A PGM-based System for Arabic Handwritten Word Recognition

A PGM-based System for Arabic Handwritten Word Recognition

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri