Multi-Font Arabic Isolated Character Recognition Using Combining Machine Learning Classifiers

Abstract

Nowadays, optical character recognition is one of the most successful automatic pattern recognition applications. Many works have been done regarding the identification of Latin and Chinese characters. However, the reason for having few investigations for the recognition of Arabic characters is the complexity and difficulty of Arabic characters identification compared to the others. In the current work, we investigate combining multiple machine learning algorithms for multi-font Arabic isolated characters recognition, where imperfect and dimensionally variable input charactersare faced. To the best of our knowledge, there is no such work yet available in this regard. Experimental results show that combined multiple classifiers can outperform each individual classifier produces by itself. The current findings are encouraging and opens the door for further research tasks in this direction.