HMMs with Explicit State Duration Applied to Handwritten Arabic Word Recognition

This paper describes an off-line segmentation-free handwritten Arabic words recognition system. The described system uses discrete HMMs with explicit state duration of various kinds (Gauss, Poisson and gamma) for the word classification purpose. After preprocessing, the word image is analyzed from right to left in order to extract from it a sequence of feature vectors. Then, vector quantization is applied to this sequence and its output is submitted to a HMMs classifier based on a likelihood criterion for identifying the word using the viterbi algorithm. Several experiments were performed using the IFN/ENIT benchmark database, they showed, on the one hand, a substantial improvement in the recognition rate when HMMs with explicit state duration of either discrete or continuous distribution are used instead of classical HMMs (i.e. with implicit state duration), on the other hand, the gamma distribution for the state duration, that have given the best recognition rate (91.23 % in top 2), seems more suitable for the HMMs based modeling of Arabic handwriting