Off-line recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwritten words recognition. The developed method uses multiple sources of information at the description and the classification levels. A hybrid planar Markovien modelling permitting to follow the horizontal and vertical variations of the writing has been adopted. This modelling is based on different levels of segmentation: horizontal, natural and vertical. The process of segmentation conducts to the decomposition of the writing in a limited set of elementary entities, with simplified morphologies specific to every horizontal band. The choice of different type of primitives is then imposed in order to assure an efficient description. Different architectures of modelling proved also to be indispensable. The classification is finally achieved using a Planar Hidden Markov Model.

Off-line recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwritten words recognition. The developed method uses multiple sources of information at the description and the classification levels. A hybrid planar Markovien modelling permitting to follow the horizontal and vertical variations of the writing has been adopted. This modelling is based on different levels of segmentation: horizontal, natural and vertical. The process of segmentation conducts to the decomposition of the writing in a limited set of elementary entities, with simplified morphologies specific to every horizontal band. The choice of different type of primitives is then imposed in order to assure an efficient description. Different architectures of modelling proved also to be indispensable. The classification is finally achieved using a Planar Hidden Markov Model.

Off-line recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwritten words recognition. The developed method uses multiple sources of information at the description and the classification levels. A hybrid planar Markovien modelling permitting to follow the horizontal and vertical variations of the writing has been adopted. This modelling is based on different levels of segmentation: horizontal, natural and vertical. The process of segmentation conducts to the decomposition of the writing in a limited set of elementary entities, with simplified morphologies specific to every horizontal band. The choice of different type of primitives is then imposed in order to assure an efficient description. Different architectures of modelling proved also to be indispensable. The classification is finally achieved using a Planar Hidden Markov Model.