Abstract
Sign language is the native form of expression used by deaf people in the world. With the recognition techniques applied to sign language, a significant need for developing tools to facilitate the accessibility of information to the deaf public has arisen. Little work deals with recognizing Moroccan sign language (MoSL) for the Moroccan deaf community. In this paper, a deep learning architecture is presented to be used to recognize MoSL signs. The proposed system uses 3D convolution neural networks to describe effectively video sequences containing Moroccan signs. Experiments showed that the system is able reliably to recognize Moroccan word signs, with 99.60% of accuracy.
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Boukdir, A., Benaddy, M., Meslouhi, O.E., Kardouchi, M., Akhloufi, M. (2023). Moroccan Sign Language Video Recognition with Deep Learning. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 447. Springer, Singapore. https://doi.org/10.1007/978-981-19-1607-6_36
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DOI: https://doi.org/10.1007/978-981-19-1607-6_36
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