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Sign language recognition (SLR) is a challenging, but highly important research field for several computer vision systems that attempt to facilitate the communication among the deaf and hearing impaired people. In this work, we propose an accurate and robust deep learning-based methodology for sign language recognition from video sequences. Our novel method relies on hand and body skeletal features extracted from RGB videos and, therefore, it acquires highly discriminative for gesturedoi:10.5281/zenodo.1422441 fatcat:7ysijegdkfgfhby6qejlfbtyqe