In this paper, we investigate a range of
strategies for combining multiple machine learning
techniques for recognizing Arabic characters, where we
are faced with imperfect and dimensionally variable input
characters. Experimental results show that combined
confidence-based backoff strategies can produce more
accurate results than each technique produces by itself
and even the ones exhibited by the majority voting
combination.