JUMLA-QSL-22: A dataset of Qatari sign language sentences

Sign languages are the most common mode of communication with and between hearing-impaired individuals. In the Arab world, Arabic sign language is used with different dialects supporting a distinct set of rules for the gestures used. With research on natural language processing advancing, models have been developed to translate sign language to spoken language and vice versa. However, Arabic sign language has rarely been studied due to the lack of availability of datasets dealing with Arabic sign language.The aim of this project is to improve the accessibility of hearing-impaired individuals by bridging the gap in communication using the Jumla dataset. This dataset supplies a large sample of Arabic sign language in the Qatari dialect, having 6300 records collected over a period of 5 months. 7 participants were invited to the study which included 5 hearing-impaired individuals and 2 sign language interpreters. The participants were given a sentence from a list of 900 sentences at a time and videos of them signing the sentences in Qatari sign language were recorded. The videos were recorded from four angles (front, left side, right side, and top view) using four true-depth cameras.