TY - JFULL AU - Salim Ouchtati and Jean Sequeira and Mouldi Bedda PY - 2015/9/ TI - A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments T2 - International Journal of Computer and Information Engineering SP - 1976 EP - 1983 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10002726 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 104, 2015 N2 - 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. ER -