Sign languages are used as a primary language by approximately 70 million
D/deaf people world-wide. However, most communication technologies operate in
spoken and written languages, creating inequities in access. To help tackle
this problem, we release ASL Citizen, the largest Isolated Sign Language
Recognition (ISLR) dataset to date, collected with consent and containing
83,912 videos for 2,731 distinct signs filmed by 52 signers in a variety of
environments. We propose that this dataset be used for sign language dictionary
retrieval for American Sign Language (ASL), where a user demonstrates a sign to
their own webcam with the aim of retrieving matching signs from a dictionary.
We show that training supervised machine learning classifiers with our dataset
greatly advances the state-of-the-art on metrics relevant for dictionary
retrieval, achieving, for instance, 62% accuracy and a recall-at-10 of 90%,
evaluated entirely on videos of users who are not present in the training or
validation sets. An accessible PDF of this article is available at
this https URL

发布了迄今最大的孤立手语识别数据集 ASL Citizen，该数据集包含 83,912 个视频，由 52 名手语用户在各种环境中拍摄了 2,731 个不同的手势，并提出应将该数据集用于手语字典检索，使用我们数据集训练监督学习分类器在手语字典检索方面极大地提高了性能。