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Automatic sign language identification

Gebre, B. G., Wittenburg, P., & Heskes, T. (2013). Automatic sign language identification. In Proceeding of the 20th IEEE International Conference on Image Processing (ICIP) (pp. 2626-2630).
We propose a Random-Forest based sign language identification system. The system uses low-level visual features and is based on the hypothesis that sign languages have varying distributions of phonemes (hand-shapes, locations and movements). We evaluated the system on two sign languages -- British SL and Greek SL, both taken from a publicly available corpus, called Dicta Sign Corpus. Achieved average F1 scores are about 95% - indicating that sign languages can be identified with high accuracy using only low-level visual features.
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