In the field of multimodal communication, sign language isand continues to be, one of the most understudied areas. Thanks to therecent advances in the field of deep learning, there are far-reaching implications and applications that neural networks can have for sign languagemastering. This paper describes a method for ASL alphabet recognitionusing Convolutional Neural Networks (CNN), which allows to monitoruser’s learning progress. American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity inASL signs, high interclass similarities, large intraclass variations, andconstant occlusions. We produced a robust model that classifies letterscorrectly in a majority of cases. The experimental results encouraged usto investigate the adoption of AI techniques to support learning of a signlanguage, as a natural language with its own syntax and lexicon. Thechallenge was to deliver a mobile sign language training solution thatusers may adopt during their everyday life. To satisfy the indispensableadditional computational resources to the locally connected end- userdevices, we propose the adoption of a Fog-Computing Architecture.
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