Clonal Selection Classification Algorithm Applied To Arabic Writer Identification

Artificial Immune Systems (AIS) is emerging bio-inspired computer science techniques which embody the principles of biological immune systems for tackling complex real-world problems such as pattern recognition. Among the several immune-computing models, Clonal Selection Classification Algorithm (CSCA) is used for several classification problems. Meanwhile, the issues related to writer identification are currently at the heart of numerous concerns in our modern day's society. Writer identification for Arabic text is receiving a renewed attention. Many popular machine learning techniques have been used in writer identification systems but only one limited attempt has been done with AIS. In this paper, we apply CSCA algorithms to perform Arabic writer identification based on a set of features extracted from Grey Level Co-occurrence Matrices. Some feature selection techniques are applied to improve computation time. Four traditional classifiers have also been used in our experiments for performance comparison. The obtained results show the promising ability of CSCA-Based Classifier in Writer identification.