Recognition of Handwritten Arabic words using a neuro-fuzzy network
- Departement de Genie electrique, Universite 08 Mai 45 de Guelma (Algeria)
- Departement d'Electronique, Universite Mentouri de Constantine (Algeria)
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
- OSTI ID:
- 21143559
- Journal Information:
- AIP Conference Proceedings, Vol. 1019, Issue 1; Conference: CISA 08: 1. Mediterranean conference on intelligent systems and automation, Annaba (Algeria), 30 Jun - 2 Jul 2008; Other Information: DOI: 10.1063/1.2952988; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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