Abstract—In this paper we present an off line system for the recognition of the isolated handwritten digits. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm and fed by several feature vectors. The used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: the distribution sequence, sondes application, the Barr features, The central moments of image coding according to the directions of Freeman, and the centred moments of the different projections and profiles.

Index Terms—Optical characters recognition, neural networks, barr features, image processing, pattern recognition, features extraction.

Salim Ouchtati is with the Electronic Research Laboratory of Skikda University, Route El Hadaik, Bp: 26 Skikda 21000, Algeria (e-mail: ouchtatisalim@yahoo.fr).
Mohammed Rdjimi is with Computer Science Department, Skikda University, Route El Hadaik, Bp: 26 Skikda 21000, Algeria (e-mail: medredjimi@gmail.com).
Mouldi Bedda is with the Electrical Engineering Department, Al-Jouf University, Arabie Saoudite (e-mail: mouldi_bedda@yahoo.fr).

Cite:S. Ouchtati, M. Redjimi, and M. Bedda, "A Set of Features Extraction Methods for the Recognition of the Isolated Handwritten Digits," International Journal of Computer and Communication Engineering vol. 3, no. 5, pp. 349-355, 2014.