With the increasing availability of pen-based user interfaces, we often come upon multiple data sets of online handwritten scripts such as letters, words, etc., that are collected based on a viable interface. In this paper, we set forward a new method for online handwritten Arabic scripts recognition. Departing from the assumption that handwritten scripts are encoded as a set of strokes, the proposed approach relies first upon classifying strokes contained on the script and then recognizes the whole script. For stroke classification, an support vector machine (SVM) is trained with stroke features vectors obtained from the Beta-elliptic model and fuzzy elementary perceptual codes to obtain class stroke probabilities. The output of this SVM is combined with spatial relation vectors feeding to a second SVM to provide scripts level recognition. The proposed model has been tested on MAYASTROUN dataset. In order to obtain additional insight into the efficiency of the proposed approach, we performed further experiments on ADAB data set. The experimental results highlight its relevance by comfortably outperforming state-of-art systems.

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