Abstract
This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing.By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset.The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples.Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras.Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates.Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech.FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/