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Due to the omnipresence of mobile devices, online handwritten scripts havebecome the most important feeding input to smartphones and tablet devices. Toincrease online handwriting recognition performance, deeper neural networkshave extensively been used. In this context, our paper handles the problem ofonline handwritten script recognition based on extraction features system anddeep approach system for sequences classification. Many solutions have appearedin order to facilitate the recognition of handwriting. Accordingly, we used anexistent method and combined with new classifiers in order to get a flexiblesystem. Good results are achieved compared to online characters and wordsrecognition system on Latin and Arabic scripts. The performance of our twoproposed systems is assessed by using five databases. Indeed, the recognitionrate exceeds 98%.

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition