Recognition of Handwritten Arabic words using a neuro-fuzzy network

Journal Article · Thu Jun 12 04:00:00 EDT 2008 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2952988· OSTI ID:21143559

Boukharouba, Abdelhak ^[1]; Bennia, Abdelhak ^[2]

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1. Departement de Genie electrique, Universite 08 Mai 45 de Guelma (Algeria)
2. 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, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 1019; ISSN 0094-243X; ISSN APCPCS
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
DESIGN
FUZZY LOGIC
NEURAL NETWORKS
PATTERN RECOGNITION
PROGRAMMING LANGUAGES
TOPOLOGY