In this paper we present a system for the off-line recognition of cursive Arabic handwritten words. This system
in an enhanced version of our reference system presented in [El-Hajj et al., 05] which is based on Hidden Markov
Models (HMMs) and uses a sliding window approach. The enhanced version proposed here uses contextual
character models. This approach is motivated by the fact that the set of Arabic characters includes a lot of ascending
and descending strokes which overlap with one or two neighboring characters. Additional character models are
constructed according to characters in their left or right neighborhood. Our experiments on images of the benchmark
IFN/ENIT database of handwritten villages/towns names show that using contextual character models improves
recognition. For a lexicon of 306 name classes, accuracy is increased by 0.6% in absolute value which corresponds
to a 7.8% reduction in error rate.
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