The Ankara University Turkish Sign Language Dataset (AUTSL) is a large-scale, multimode dataset that contains isolated Turkish sign videos. It contains 226 signs that are performed by 43 different signers. There are 38,336 video samples in total. The samples are recorded using Microsoft Kinect v2 in RGB, depth and skeleton formats. The videos are provided at a resolution of 512×512. The skeleton data contains spatial coordinates, i.e. (x, y), of the 25 junction points on the signer body that are aligned with 512×512 data.
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BosphorusSign22k is a benchmark dataset for vision-based user-independent isolated Sign Language Recognition (SLR). The dataset is based on the BosphorusSign (Camgoz et al., 2016c) corpus which was collected with the purpose of helping both linguistic and computer science communities. It contains isolated videos of Turkish Sign Language glosses from three different domains: Health, finance and commonly used everyday signs. Videos in this dataset were performed by six native signers, which makes this dataset valuable for user independent sign language studies.
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