Mustafa S. Kadhm, Asst. Prof. Dr. Alia Karim Abdul Hassan
Abstract
this paper proposed a new architecture for
handwriting word recognition system Based on Support Vector
Machine SVM Classifier. The proposed work depends on the
handwriting word level, and it does not need for character
segmentation stage. An Arabic handwriting dataset AHDB,
dataset used for train and test the proposed system. Besides, the
system achieved the best recognition accuracy 96.317% based on
several feature extraction methods and SVM classifier.
Experimental results show that the polynomial kernel of SVM is
convergent and more accurate for recognition than other SVM
kernels
Publication Date
11/6/2015
Publisher
International Journal of Advanced Computer Science & Applications
أ.م.د مصطفى سلام كاظم | Mustafa Salam Kadhm | 15426