Over a period of three years (2009 - 2011) the daily news and weather forecast airings of the German public tv-station PHOENIX featuring sign language interpretation have been recorded and the weather forecasts of a subset of 386 editions have been transcribed using gloss notation. Furthermore, we used automatic speech recognition with manual cleaning to transcribe the original German speech. As such, this corpus allows to train end-to-end sign language translation systems from sign language video input to spoken language. The signing is recorded by a stationary color camera placed in front of the sign language interpreters. Interpreters wear dark clothes in front of an artificial grey background with color transition. All recorded videos are at 25 frames per second and the size of the frames is 210 by 260 pixels. Each frame shows the interpreter box only.

WLASL is a large video dataset for Word-Level American Sign Language (ASL) recognition, which features 2,000 common different words in ASL. Source: https://github.com/dxli94/WLASL

CSL-Daily (Chinese Sign Language Corpus) is a large-scale continuous SLT dataset. It provides both spoken language translations and gloss-level annotations. The topic revolves around people's daily lives (e.g., travel, shopping, medical care), the most likely SLT application scenario. [1] Improving Sign Language Translation with Monolingual Data by Sign Back-Translation, CVPR, 2021.

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.

The Ankara University Turkish Sign Language Dataset (AUTSL) is a large-scale, multimode dataset that contains isolated Turkish sign videos. It contains 226 signs that are performed by 43 different signers. There are 38,336 video samples in total. The samples are recorded using Microsoft Kinect v2 in RGB, depth and skeleton formats. The videos are provided at a resolution of 512×512. The skeleton data contains spatial coordinates, i.e. (x, y), of the 25 junction points on the signer body that are aligned with 512×512 data. Source: AUTSL Dataset

The signing is recorded by a stationary color camera placed in front of the sign language interpreters. Interpreters wear dark clothes in front of an artificial grey background with color transition. All recorded videos are at 25 frames per second and the size of the frames is 210 by 260 pixels. Each frame shows the interpreter box only.

BOBSL is a large-scale dataset of British Sign Language (BSL). It comprises 1,962 episodes (approximately 1,400 hours) of BSL-interpreted BBC broadcast footage accompanied by written English subtitles. From horror, period and medical dramas, history, nature and science documentaries, sitcoms, children’s shows and programs covering cooking, beauty, business and travel, BOBSL covers a wide range of topics. The dataset features a total of 39 signers. Distinct signers appear in the training, validation and test sets for signer-independent evaluation. Description from: BOBSL: BBC-Oxford British Sign Language Dataset

This is the home of a collaborative data collection effort by U. Chicago and TTI-Chicago researchers. This is to our knowledge the first collection of American Sign Language fingerspelling data "in the wild," that is in naturally occurring (online) video. The ChicagoFSWild data set contains 7304 ASL fingerspelling sequences signed by 160 signers, carefully annotated by students who have studied ASL.

This is the home of a collaborative data collection effort by U. Chicago and TTI-Chicago researchers. This is to our knowledge the first collection of American Sign Language fingerspelling data "in the wild," that is in naturally occurring (online) video. The collection consists of two data set releases, ChicagoFSWild and ChicagoFSWild+. The ChicagoFSWild data set contains 7304 ASL fingerspelling sequences signed by 160 signers, carefully annotated by students who have studied ASL. ChicagoFSWild+ contains 55,232 fingerspelling sequences signed by 260 signers.

Dataset Description The Greek Sign Language (GSL) is a large-scale RGB+D dataset, suitable for Sign Language Recognition (SLR) and Sign Language Translation (SLT). The video captures are conducted using an Intel RealSense D435 RGB+D camera at a rate of 30 fps. Both the RGB and the depth streams are acquired in the same spatial resolution of 848×480 pixels. To increase variability in the videos, the camera position and orientation is slightly altered within subsequent recordings. Seven different signers are employed to perform 5 individual and commonly met scenarios in different public services. The average length of each scenario is twenty sentences. The dataset contains 10,290 sentence instances, 40,785 gloss instances, 310 unique glosses (vocabulary size) and 331 unique sentences, with 4.23 glosses per sentence on average. Each signer is asked to perform the pre-defined dialogues five consecutive times. In all cases, the simulation considers a deaf person communicating with a single public service employee. The involved signer performs the sequence of glosses of both agents in the discussion. For the annotation of each gloss sequence, GSL linguistic experts are involved. The given annotations are at individual gloss and gloss sequence level. A translation of the gloss sentences to spoken Greek is also provided. ## Evaluation The GSL dataset includes the 3 evaluation setups: - Signer-dependent continuous sign language recognition (GSL SD) – roughly 80% of videos are used for training, corresponding to 8,189 instances. The rest 1,063 (10%) were kept for validation and 1,043 (10%) for testing. - Signer-independent continuous sign language recognition (GSL SI) – the selected test gloss sequences are not used in the training set, while all the individual glosses exist in the training set. In GSL SI, the recordings of one signer are left out for validation and testing (588 and 881 instances, respectively). The rest 8821 instances are utilized for training. - Isolated gloss sign language recognition (GSL isol.) – The validation set consists of 2,231 gloss instances, the test set 3,500, while the remaining 34,995 are used for training. All 310 unique glosses are seen in the training set. For more info and results, advice our paper ## Paper Abstract: A Comprehensive Study on Sign Language Recognition Methods, Adaloglou et al. 2020 In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural network methods in this field, a thorough evaluation on multiple publicly available datasets is performed. The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses. For this task, two new sequence training criteria, known from the fields of speech and scene text recognition, are introduced. Furthermore, a plethora of pretraining schemes are thoroughly discussed. Finally, a new RGB+D dataset for the Greek sign language is created. To the best of our knowledge, this is the first sign language dataset where sentence and gloss level annotations are provided for every video capture. Arxiv link

MSASL is a real-life large-scale sign language data set comprising over 25,000 annotated videos. Source: MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language

BosphorusSign22k is a benchmark dataset for vision-based user-independent isolated Sign Language Recognition (SLR). The dataset is based on the BosphorusSign (Camgoz et al., 2016c) corpus which was collected with the purpose of helping both linguistic and computer science communities. It contains isolated videos of Turkish Sign Language glosses from three different domains: Health, finance and commonly used everyday signs. Videos in this dataset were performed by six native signers, which makes this dataset valuable for user independent sign language studies. Source: BosphorusSign22k Sign Language Recognition Dataset

YouTube-ASL is a large-scale, open-domain corpus of American Sign Language (ASL) videos and accompanying English captions drawn from YouTube. With ~1000 hours of videos and >2500 unique signers, YouTube-ASL is ~3x as large and has ~10x as many unique signers as the largest prior ASL dataset.

The sign database for the Argentinian Sign Language, created with the goal of producing a dictionary for LSA and training an automatic sign recognizer, includes 3200 videos where 10 non-expert subjects executed 5 repetitions of 64 different types of signs. Signs were selected among the most commonly used ones in the LSA lexicon, including both verbs and nouns.

Content4All is a collection of six open research datasets aimed at automatic sign language translation research. Sign language interpretation footage was captured by the broadcast partners SWISSTXT and VRT. Raw footage was anonymized and processed to extract 2D and 3D human body pose information. From the roughly 190 hours of processed data, three base (RAW) datasets were released, namely 1) SWISSTXT-RAW-NEWS, 2) SWISSTXT-RAW-WEATHER and 3) VRT-RAW. Each dataset contains sign language interpretations, corresponding spoken language subtitles, and extracted 2D/3D human pose information. A subset from each base dataset was selected and manually annotated to align spoken language subtitles and sign language interpretations. The subset selection was done to resemble the benchmark Phoenix 2014T dataset. Our aim is for these three new annotated public datasets, namely 4) SWISSTXT-NEWS, 5) SWISSTXT-WEATHER and 6) VRT-NEWS to become benchmarks and underpin future research as the field moves closer to translation and production on larger domains of discourse.

About Dataset Step right up to our AI data collection company, where we’ve got something special just for you: a unique set of American Sign Language datasets! These datasets are carefully curated to give your AI projects a powerful boost. American Sign Language Dataset Unlocking the World of American Sign Language: Welcome to Globose Technology Solutions Private Limited, where we understand the significance of American Sign Language (ASL) in breaking communication barriers and fostering inclusivity. Our ASL datasets are a treasure trove for researchers, tech enthusiasts, and AI aficionados eager to innovate in ASL-based applications. Join us as we unlock the world of American Sign Language together! Why Choose Our Collection: Unparalleled Diversity: Our datasets cover a wide array of sign language gestures, ensuring we’ve got all bases covered for different ASL signs and expressions. Impeccable Quality: We’re all about quality. Our datasets feature high-quality images and videos, making them perfect for accurate and dependable model training. Ethical Data Sourcing: We prioritize ethics. Our data collection process is ethical, respecting the privacy and consent of all participants involved. User-Friendly Accessibility: We’ve made sure our datasets are super user-friendly and seamlessly integrate into your AI projects. Constant Updates: We’re dedicated to keeping our ASL datasets fresh with the latest sign language gestures and expressions. American Sign Language Dataset Explore Our Collection: To access our exclusive American Sign Language datasets, please visit our dataset download page: https://datasetdownloadslinks.s3.us-east-1.amazonaws.com/Download%20Datasets/American%20Sign%20Language%20Dataset.zip. We believe in the power of AI to transform lives, and our ASL datasets are just one example of how we’re making that vision a reality. Conclusion At Globose Technology Solutions, we’re committed to ethical AI practices. We’ve gathered American Sign Language data to ensure our technology is inclusive. If you’re interested in seeing an AI company making a positive impact responsibly, check out our ASL datasets. This dataset is sourced from Gts.ai

Rendered Handpose Dataset contains 41258 training and 2728 testing samples. Each sample provides: - RGB image (320x320 pixels) - Depth map (320x320 pixels) - Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm - 21 Keypoints for each hand with their uv coordinates in the image frame, xyz coordinates in the world frame and a visibility indicator - Intrinsic Camera Matrix K Source: Learning to Estimate 3D Hand Pose from Single RGB Images Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html

An artificial corpus built using grammatical dependencies rules due to the lack of resources for Sign Language. Source: ASLG-PC12

Extremely important: The ASLLVD video data are subject to Terms of Use: http://www.bu.edu/asllrp/signbank-terms.pdf. By downloading these video files, you are agreeing to respect these conditions. In particular, NO FURTHER REDISTRIBUTION OF THESE VIDEO FILES is allowed. ---------- The American Sign Language Lexicon Video Dataset (ASLLVD) consists of videos of >3,300 ASL signs in citation form, each produced by 1-6 native ASL signers, for a total of almost 9,800 tokens. This dataset includes multiple synchronized videos showing the signing from different angles. Linguistic annotations include gloss labels, sign start and end time codes, start and end handshape labels for both hands, morphological and articulatory classifications of sign type. For compound signs, the dataset includes annotations for each morpheme. To facilitate computer vision-based sign language recognition, the dataset also includes numeric ID labels for sign variants, video sequences in uncompressed-raw format, and camera calibration sequences.

The Azerbaijani Sign Language Dataset (AzSLD) is a comprehensive, large dataset designed to facilitate the development and evaluation of machine learning models for the recognition and translation of Azerbaijani Sign Language (AzSL). AzSLD is the first publicly available dataset focused on Azerbaijani Sign Language. It contributes to the global effort to improve accessibility for the deaf and hard-of-hearing community in Azerbaijan. The dataset aims to bridge the gap between technology and accessibility by providing high-quality data for researchers, developers, and practitioners working on sign language recognition or translation systems. The data collection costs are covered by the "Strengthening Data Analytics Research and Training Capacity through Establishment of dual Master of Science in Computer Science and Master of Science in Data Analytics (MSCS/DA) degree program at ADA University" project, funded by BP and the Ministry of Education of the Republic of Azerbaijan. Dataset Composition AzSLD is organized into three primary components: 1. AzSLD_Sentences This component contains video sequences of complete sentences in AzSL. It is designed to capture the fluidity and contextual nature of sign language, providing data for more complex language modeling tasks. It includes over 60 hours of high-definition video recordings, annotated with timestamped glosses for 500 distinct classes, enabling precise analysis and robust model training. Ground truth annotations of sentences for each class were added in a separate file. The videos were performed by 18 to 25 different signers, with a slight imbalance among them. 2. AzSLD_Words This component comprises a collection of short video samples representing frequently used words in Azerbaijani Sign Language. It is divided into two subsets: AzSLD_Words_100: Contains 100 commonly used words in AzSL. AzSLD_Words_200: Extends the first subset, including all 100 words from AzSLD_Words_100 along with an additional 100 words, for a total of 200 words. Folder names indicate the ground truth labels for the ease of word-level model evaluation. 3. AzSLD_Fingerspelling This component includes over 14,000 video and image samples of letters of the Azerbaijani alphabet. Each sign is captured from multiple angles to ensure comprehensive coverage of dactylology in AzSL. This component is ideal for tasks involving letter recognition and the integration of fingerspelling into broader sign language recognition systems. Key Features Double-View Recordings The dataset includes 10,104 synchronized video recordings from two camera angles to capture both frontal and side views of hand and body movements, ensuring that the subtle nuances of sign language are well-represented. Diverse Signers The dataset features recordings from a diverse group of native AzSL signers, encompassing variations in age, gender, and signing style. This diversity is crucial for training models that are robust to variations in signing. Detailed Annotations Each video is annotated with comprehensive metadata, including the sign’s label (dactyl, word, or sentence), signer ID, and timestamped glosses for sentence-level signs. High-Quality Data Format The dataset comprises RGB videos in high-definition (HD) resolution at 35 frames per second, accompanied by JSON files containing annotations and metadata. The data is systematically organized into folders by category for ease of navigation. Ethical Transparency All participants provided informed consent for collecting, publishing, and using the data, ensuring compliance with ethical research standards. Accessibility The AzSLD is available under Creative Commons Attribution 4.0 International with free access for academic research through Zenodo. Citation: When using AzSLD in your research, please cite the following paper: Alishzade, N., Hasanov, J. (2024). AzSLD: Azerbaijani Sign Language Dataset for Dactyl, Word, and Sentence Translation with Baseline Software. [Journal Name], [Volume(Issue)], [Pages]. DOI: [DOI link]. The preprint is available at: https://arxiv.org/abs/2411.12865 Contact: For questions, feedback, or contributions, please contact the project team at: slr.project.ada@gmail.com

We introduce a video dataset Bukva for Russian Dactyl Recognition task. Bukva dataset size is about 27 GB, and it contains 3757 RGB videos with more than 101 samples for each RSL alphabet sign, including dynamic ones. The dataset is divided into training set and test set by subject user_id. The training set includes 3097 videos, and the test set includes 660 videos. The total video recording time is ~4 hours. About 17% of the videos are recorded in HD format, and 70% of the videos are in FullHD resolution.

A large-scale isolated Indian sign language dataset. It contains 2002 common words, used in daily communications among Indian deaf community. The dataset contains 40033 videos across 2002 words. The total duration of the dataset is around 36.2 hours with 7.8 Million frames.

A multimodal LIBRAS-UFOP Brazilian sign language dataset of minimal pairs using a microsoft Kinect senso. The dataset is based on the concept of minimal pairs, which follows specific categorization criteria: - The signs are labeled correctly, and validated by an expert in sign language; - The dataset presents complete RGB-D and skeleton data. - The dataset consists of 56 different signs with high similarity grouped into four categories

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.

Brazilian Sign Language (Libras) data set with 20 signs for sign language and gesture recognition benchmark: - Acontecer (To happen) - Aluno (Student) - Amarelo (Yellow) - América (America) - Aproveitar (To enjoy) - Bala (Candy) - Banco (Bank) - Banheiro (Bathroom) - Barulho (Noise) - Cinco (Five) - Conhecer (To know) - Espelho (Mirror) - Esquina (Corner) - Filho (Son) - Maçã (Apple) - Medo (Fear) - Ruim (Bad) - Sapo (Frog) - Vacina (Vaccine) - Vontade (Will) Each one of the signs was recorded 5 times by 12 signers, using a Chroma Key background. Among the signers are men and women with basic to advanced knowledge in BSL. Videos are in 1920 x 1080 MP4 files (30 fps). Disclaimer: Signer 3 is missing: - Aluno (Student) - América (America) - Cinco (Five) Signer 4 is missing: - Filho (Son) Signer 9 is missing: - Amarelo (Yellow) - Banheiro (Bathroom) - Conhecer (To know) - Esquina (Corner) - Medo (Fear) Signer 7 has extra recordings: - Barulho (Noise) - 2 extra - Filho (Son) - 1 extra Former name: Libras-20

A Chinese sign language dataset that includes dialogue information.

We introduce a large-scale video dataset Slovo for Russian Sign Language task. Slovo dataset size is about 16 GB, and it contains 20400 RGB videos for 1000 sign language gestures from 194 singers. Each class has 20 samples. The dataset is divided into training set and test set by subject user_id. The training set includes 15300 videos, and the test set includes 5100 videos. The total video recording time is ~9.2 hours. About 35% of the videos are recorded in HD format, and 65% of the videos are in FullHD resolution. The average video length with gesture is 50 frames. Annotation file is easy to use and contains some useful columns, see annotations.csv file: | | attachment_id | user_id | width | height | length | text | train | begin | end | |---:|:--------------|:--------|------:|-------:|-------:|-------:|:--------|:------|:----| | 0 | de81cc1c-... | 1b... | 1440 | 1920 | 14 | привет | True | 30 | 45 | | 1 | 3c0cec5a-... | 64... | 1440 | 1920 | 32 | утро | False | 43 | 66 | | 2 | d17ca986-... | cf... | 1920 | 1080 | 44 | улица | False | 12 | 31 | where: - attachment_id - video file name - user_id - unique anonymized user ID - width - video width - height - video height - length - video length - text - gesture class in Russian Langauge - train - train or test boolean flag - begin - start of the gesture (for original dataset) - end - end of the gesture (for original dataset)

The first and the one open dataset for Russian finger- spelling, contained 1,593 annotated phrases and over 37 thousand HD+ videos.

The ASL-Phono introduces a novel linguistics-based representation, which describes the signs in the ASLLVD dataset in terms of a set of attributes of the American Sign Language phonology.

The ASL-Skeleton3D introduces a representation based on mapping into the three-dimensional space the coordinates of the signers in the ASLLVD dataset. This enables a more accurate observation of the body parts and the signs articulation, allowing researchers to better understand the language and explore other approaches to the SLR field.