The How2Sign is a multimodal and multiview continuous American Sign Language (ASL) dataset consisting of a parallel corpus of more than 80 hours of sign language videos and a set of corresponding modalities including speech, English transcripts, and depth. A three-hour subset was further recorded in the Panoptic studio enabling detailed 3D pose estimation.

Sign Language Datasets for French Belgian Sign Language This dataset is built upon the work of Belgian linguists from the University of Namur. During eight years, they've collected and annotated 50 hours of videos depicting sign language conversation. 100 signers were recorded, making it one of the most representative sign language corpus. The annotation has been sanitized and enriched with metadata to construct two, easy to use, datasets for sign language recognition. One for continuous sign language recognition and the other for isolated sign recognition. ## LSFB-CONT The dataset for continuous sign language recognition is made of over 25h of video clips. Each clip is associated with a time-aligned annotation file containing the start and the end of each sign along with a gloss (label) associated with all unique signs. Mediapipe pose and hands information were also computed for each video clip and these metadata are made available in the dataset. ## LSFB-ISOL The isolated version of the dataset contains only clips showing one isolated sign issued from the LSFB-CONT dataset. We chose to keep all the signs with at least 40 examples, leading to a dataset containing over 50 000 clips for 635 different glosses (labels). The Mediapipe metadata is also available for this dataset.

Mediapi-RGB is a bilingual corpus of French Sign Language (LSF) and written French in the form of subtitled videos, accompanied by complementary data (various representations, segmentation, vocabulary, etc.). It can be used in academic research for a wide range of tasks, such as training or evaluating sign language (SL) extraction, recognition or translation models. To build this corpus, we used videos from Média'Pi!, a bilingual online media with journalistic-type content in LSF with French subtitles. We collected 1230 videos dating from September 2017 to January 2022, representing a total of 86h. Based on the subtitles, we temporally segmented the videos into 50084 video segments (or extracts). We also automatically cropped the signer and harmonised the segments in terms of size (444x444) and frequency (25fps).