A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments

Abstract : In the context of the handwriting recognition, we propose an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods. The Distribution parameters, the centered moments of the different projections of the different segments, the centered moments of the word image coding according to the directions of Freeman, and the Barr features applied binary image of the word and on its different segments. The classification is achieved by a multi layers perceptron. A detailed experiment is carried and satisfactory recognition results are reported.
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Article dans une revue
International Journal of Computer, Electrical, Automation, Control and Information Engineering, World Academy of Science, Engineering and Technology, 2015, 9, pp.1910-1916
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https://hal-amu.archives-ouvertes.fr/hal-01389849
Contributeur : Sébastien Mavromatis <>
Soumis le : dimanche 30 octobre 2016 - 07:43:30
Dernière modification le : mercredi 12 septembre 2018 - 01:27:34

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  • HAL Id : hal-01389849, version 1

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Salim Ouchtati, Jean Sequeira, Mouldi Bedda. A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments. International Journal of Computer, Electrical, Automation, Control and Information Engineering, World Academy of Science, Engineering and Technology, 2015, 9, pp.1910-1916. 〈hal-01389849〉

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