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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..

We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact that HMMs are deficient in modeling character durations properly. We will show experimentally that explicit state duration modeling in the HMM framework can significantly improve the discriminating capacity of the HMMs to deal with very difficult pattern recognition tasks such as unconstrained Arabic handwriting recognition. In order to carry out the letter and word model training and recognition more efficiently, we propose a new version of the Viterbi algorithm taking into account explicit state duration modeling. Three distributions (Gamma, Gauss, and Poisson) for the explicit state duration modeling have been used, and a comparison between them has been reported. To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image. Several experiments have been performed using the IFN/ENIT benchmark database and the best recognition performances achieved by our system outperform those reported recently on the same database.