Ensemble of Biased Learners for Offline Arabic Handwriting Recognition

Techniques and performance of text recognition systems and software has shown great improvement in recent years. OCRs now can read any machine printed document with good accuracy. However, the advancements are primarily for Latin scripts and even for such scripts performance is limited in case of handwritten documents. Little work has been done for cursive scripts such as Arabic and still there is a room for improvement both in terms of accuracy and techniques. This paper presents an algorithm to recognize handwritten Arabic text using an ensemble of biased classifiers in a hierarchical setting. We address the fundamental shortcomings of the traditional Machine Learning paradigms when applied to Arabic scripts. Experiments have been conducted on the AMA Arabic dataset to show the efficacy of our method.