Abstract—In this work we present 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 parameters of distribution, the moments centered of the different projections and the Barr features. Let's note that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.
Index Terms—Optical characters recognition, neural networks, barr features, image processing, pattern recognition, features extraction.
Salim Ouchtati is with the Skikda Electronic Laboratory, Engineering Department, Faculty of Technology, Skikda University, Route El Hadaik, Bp: 26 Skikda 21000, Algeria (Phone: 0021393935198, e-mail: ouchtatisalim@ yahoo.fr).
Mohammed Redjimi is with the Computer Science Department, Faculty of Science, Skikda University, Algeria (e-mail: redjimimed@yahoo.fr).
Mouldi Bedda is with the Electrical Engineering Department, College of Engineering Al-Jouf University, Arabie Saoudite (e-mail: mouldi_bedda@yahoo.fr).
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Cite:Salim Ouchtati, Mohammed Redjimi, and Mouldi Bedda, "Salim Ouchtati, Mohammed Redjimi, and Mouldi Bedda," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 129-134, 2014.
