These datasets are used for machine-learning research
Todo
Sign language datasets
- Co: Country
- Class: Classes
- Subj: Subjects
- LL: Language level(W-Word,S-Sentence,H-Handshape)
- Type: Type(V-Video, VR-Video(RGB), VD-Video(depth))
- An: Annotations
- Av: Availability(CA-Contact Author, PA-Publicly Available, Un-Unknown, Non-Non available)
- T: There is in our hard drive?(Y-Yes, N-No)
id | Dataset name | Co | Class | Subj | Samples | Data | LL | Type | An | Av | T |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | DGS Kinect 40 | Ger | 40 | 15 | 3000 | W | V,[9] | PA | Y | ||
2 | RWTH-PHOENIX-Weather | Ger | 1200 | 9 | 45760 | 52gb | S | V | [18] | PA | Y |
3 | SIGNUM | Ger | 450 | 25 | 33210 | 920gb | S | V | PA,[5] | N | |
4 | GSL 20 | Gre | 20 | 6 | ~840 | W | CA | Y | |||
5 | Boston ASL LVD | USA | 3300+ | 6 | 9800 | W | V,[9] | [19,20] | PA | N | |
6 | PSL Kinect 30 | Pol | 30 | 1 | 30×10=300 | ~1.2gb | W | V,[10] | PA | Y | |
7 | PSL ToF 84 | Pol | 84 | 1 | 84×20=1680 | ~33gb | W | V,[11] | PA | N | |
8 | PSL 101 | Pol | ? | ? | ? | ? | ? | ? | CA | N | |
9 | LSA64 | Arg | 64 | 10 | 3200 | 20gb | W | VR | [21] | PA | Y |
10 | BosphorusSign | Tur | Non | N | |||||||
11 | MSR Gesture 3D | USA | 12 | 10 | 336 | 28mb | W | VD | PA | N | |
12 | DEVISIGN-G | Chi | 36[1] | 8 | 432 | ? | W | VR | CA | N | |
13 | DEVISIGN-D | Chi | 500 | 8 | 6000 | ? | W | VR | CA | N | |
14 | DEVISIGN-L | Chi | 2000 | 8 | 24000 | ? | W | VR | CA | N | |
15 | IIITA -ROBITA | Ind | 23 | ? | 284mb | W | VR,[15] | CA | N | ||
16 | Purdue ASL | USA | ? | 14[3] | ? | ? | W/S | V,[14] | [6] | N | |
17 | CUNY ASL | USA | ? | 8 | ~33000[4] | ? | S | VR,[16] | [7] | U | N |
18 | SignsWorld Atlas | Ara | [2] | 10 | ? | ? | W,S,H | V,[17,14] | ? | U | N |
[1] - letters/numbers; [2] - multiple types; [3] - only 5 available; [4] - glosses; [5] - 1TB, contact author to obtain hard drive; [6] - Request DVDs/HD; [7] - Signstream; [8] - ?; [9] - multiple angles; [10]- depth from Kinect camera; [11]- ToF camera; [12]- ?; [13]- ?; [14]- RGB; [15]- 320x240; [16]- mocap data; [17]- Images; [18]- Face, hand, end/start(unfinished); [19]- Hand; [20]- end/start; [21]- Hands and Head position; [22]- only ASL fingerspelling sequences.
Dataset information and related papers
-
DGS Kinect 40 - German Sign Language
- Sign Language Recognition using Sub-Units, 2012, Cooper et al.
- Sign Language Recognition using Sequential Pattern Trees 2012, Ong et al.
- Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals 2014, Ong et al.
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RWTH-PHOENIX v1 - German Sign Language RWTH-PHOENIX v2
- Dataset paper 2012, Forster et al.
- Dataset extensions paper 2014, Forster et al
- Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers 2015, Koller et al.
- May the force be with you: Force-aligned signwriting for automatic subunit annotation of corpora 2013, Koller et al.
- Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition
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- Rapid Signer Adaptation for Continuous Sign Language Recognition Using a Combined Approach of Eigenvoices, MLLR, and MAP 2008, U. von Agris, C. Blömer, K.-F. Kraiss.
- The Significance of Facial Features for Automatic Sign Language Recognition 2008, U. von Agris, M. Knorr, K.-F. Kraiss.
- Towards a Video Corpus for Signer-Independent Continuous Sign Language Recognition 2007, U. von Agris, K.-F. Kraiss
- Rapid Signer Adaptation for Isolated Sign Language Recognition 2006, U. von Agris, D. Schneider, J. Zieren, K.-F. Kraiss.
- Advanced Man-Machine Interaction. Fundamentals and Implementation K.-F. Kraiss, ed.
- Recent Developments in Visual Sign Language Recognition 2008, U. von Agris, J. Zieren, U. Canzler, B. Bauer, K.-F. Kraiss.
- Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers 2015, Koller et al.
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Greek Sign Language (no website)
- Sign Language Recognition using Sub-Units, 2012, Cooper et al.
- Sign Language Recognition using Sequential Pattern Trees 2012, Ong et al.
- Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals 2014, Ong et al.
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Boston ASLLVD - American Sign Language
- Exploiting Phonological Constraints for Handshape Inference in ASL Video 2011, Thangali et al.
- A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling 2014 - Dilsizian - 84% accuracy
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PSL Kinect 30 - Polish Sign Language
- Polish sign language words recognition with Kinect 2013, Oszust et al.
- Some Approaches to Recognition of Sign Language Dynamic Expressions with Kinect 2014, Oszust et al.
- Recognition of Hand Gestures Observed by Depth Cameras 2015, Kapuscinski et al.
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PSL ToF 84 - Polish Sign Language
- Polish sign language words recognition with Kinect 2013,Oszust et al.
- Recognition of Hand Gestures Observed by Depth Cameras 2015, Kapuscinski et al.
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PSL 101 - Polish Sign Language (no website)
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LSA64 Argentinian Sign Language
- LSA64: an Argentinian Sign Language Dataset
- Sign Languague Recognition Without Frame-Sequencing Constraints: A Proof of Concept on the Argentinian Sign Language
- Dynamic Gesture Recognition and its Application to Sign Language 2017, Ronchetti
- SIGN LANGUAGE RECOGNITION BASED ON HAND AND BODY SKELETAL DATA 2017,Konstantinidis et al.
- Real-Time Sign Language Gesture (Word) Recognition from Video Sequences Using CNN and RNN 2018, Masood et al.
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IIITA -ROBITA Indian Sign Language Gesture Database
- Recognizing & Interpreting Indian Sign Language Gesture for Human Robot Interaction 2010, Nandy et al.
- Recognition of Isolated Indian Sign Language gesture in Real Time 2010, Nandy et al.
Datasets Handshape features (Handshape/hand posture datasets) but not all are for sign language
id | Name | Co | Clas | Sub | Samples | Data | Type | Availability |
---|---|---|---|---|---|---|---|---|
1 | ASL Fingerspelling A | USA | 24 | 5 | 131000 | 2.1gb | images (depth+rgb) | Free download |
2 | ASL Fingerspelling B | USA | 24 | 9 | 317mb | images (depth) | Free download | |
3 | LSA16 handshapes | Arg | 16 | 10 | 800 | 7mb | images (rgb) | Free download |
4 | PSL Fingerspelling ToF | Pol | 16 | 3 | 960 | ~290mb | 3D point cloud | Free download |
5 | ISL | Iri | [23] | 6 | [24] | 170mb | segmented images | Free download |
6 | RWTH-PHOENIX-Weather Handshapes | Ger | 60 | [25] | + 17gb | Hand Images (rgb) | Free download | |
7 | Japanese Fingerspelling Dataset | Jap | 41 | 10 | 8055 | 4.5mb | [26] | Free download |
8 | NUS hand posture dataset I | Sin | 10 | ? | 240 | 3mb | images(rgb),160x120 | Free download |
9 | NUS hand posture dataset II | Sin | 10 | 40 | 2000 | 73mb | images(rgb)160x120 | Free download |
10 | CIARP | - | 10 | ? | 6000 | 11mb | images(rgb)38x38 | Free download |
11 | RTWH Fingerspelling dataset | Ger | ||||||
12 | Indian Kinect | Ind | 40 | 18 | 5041 | 2gb | [27] | Free download |
13 | [ArASL] | Ara | 32 | ? | 54,049 | 64mb | images(rgb) | Free download |
14 | ChicagoFSWild | USA | [2] | 160 | ? | images(rgb) | Free download | |
15 | ChicagoFSWild+ | USA |
[ArASL] - Arabic Alphabets Sign Language Dataset; [2] - multiple types; [23]- 23 static + 3 dynamic; [24]- 58114 frames/468 videos; [25]- 3359 labelled + 17gb unlabeled [26]- segmented images (rgb), 32x32 [27]- images (rgb+depth) 640x480
Dataset information and related papers
- ASL Fingerspelling
- Spelling It Out: Real-Time ASL Fingerspelling Recognition. 2011, Pugeault et al.
- Recognition of Hand Gestures Observed by Depth Cameras. 2015, Kapuscinski et al.
- PSL Fingerspelling ToF
- Recognition of Hand Gestures Observed by Depth Cameras. 2015, Kapuscinski et al.
- LSA16 handshapes
- Handshape recognition for Argentinian Sign Language using ProbSom. 2016, Ronchetti et al.
- A Study of Convolutional Architectures for Handshape Recognition applied to Sign Language 2017, Quiroga et al.
- ISL Irish Sign Language Letters.
- A Dataset for Irish Sign Language Recognition 2017, Oliveira et al.
- A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition 2017, Oliveira et al.
- RWTH-PHOENIX-Weather 2014 MS Handshapes
- Japanese Sign Language Dataset
- Recognition of JSL Finger Spelling Using Convolutional Neural Networks 2017, Hosoe, Sako and Kwolek
- Learning Siamese Features for Finger Spelling Recognition 2017, Sako and Kwolek
- NUS hand posture dataset I
- Hand posture and face recognition using a Fuzzy-Rough Approach 2010, Pramod Kumar P, Prahlad Vadakkepat, and Loh Ai Poh
- Hand Posture Recognition Using Convolutional Neural Network
- NUS hand posture dataset II
- Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds 2013, Pisharady et al
- CIARP 2017
- RTWH Fingerspelling dataset
- Modeling Image Variability in Appearance-Based Gesture Recognition. In ECCV Workshop on Statistical Methods in Multi-Image and Video Processing
- Indian Kinect github
- Nearest neighbour classification of Indian sign language gestures using kinect camera 2016, Ansari and Harit
- Arabic Alphabets Sign Language Dataset (ArASL)
- Arabic Alphabet and Numbers Sign Language Recognition
- ChicagoFSWild
- ChicagoFSWild+
Continuous hand pose
Datasets of facial features
Datasets of lip reading features
- GRID corpus - Lip
- AVICAR - Lip
- AVLetter
- CUAVE
- OuluVS1 (no website)
- OuluVS2
- BBC TV
The table from the paper - LipNet: End-to-End Sentence-level Lipreading
Method | Dataset | Size | Output | Accuracy |
---|---|---|---|---|
Fu et al. (2008) | AVICAR | 851 | Digits | 37.9% |
Hu et al. (2016) | AVLetter | 78 | Alphabet | 64.6% |
Papandreou et al. (2009) | CUAVE | 1800 | Digits | 83.0% |
Chung & Zisserman (2016a) | OuluVS1 | 200 | Phrases | 91.4% |
Chung & Zisserman (2016b) | OuluVS2 | 520 | Phrases | 94.1% |
Chung & Zisserman (2016a) | BBC TV | > 400000 | Words | 65.4% |
Gergen et al. (2016) | GRID | 29700 | Words | 86.4% |
LipNet | GRID | 28775 | Sentences | 95.2% |
Datasets of emotion reading features |
- Предобученные модели распознавания эмоций EmoPy выложили в открытый доступ
Neurohive
- F2ED: датасет для распознавания эмоций на лице https://neurohive.io/ru/novosti/f2ed-dataset-dlya-raspoznavaniya-emocij-na-lice/?fbclid=IwAR3krXoMfAJySGuZAQsVkDwPoNIfex44EgLvDJCK5-24kX9hhVYzV_7WS4E
Other info Kevin Murphy mantains a similar list for Action Recognition Datasets. Other similar websites with sign language dataset compilations are:
Papers that cite datasets that are unavailable:
- 480 signs, Indian Sign Language
- Segment, Track, Extract, Recognize and Convert Sign Language Videos to Voice/Text 2012, Kishore and Kumar
- Selfie video based continuous Indian sign language recognition system 2017, Rao and Kishore
- 10 signs, indian sign language
- 24 static handshapes, Indian Sign Language
- Recognition of Indian Sign Language in Live Video 2013, Singha and Das Hand movement datasets (movement only):
References
https://facundoq.github.io/unlp/sign_language_datasets/index.html- https://facundoq.github.io/guides/sign_language_datasets/slr
https://github.com/Nikhilkohli1/Real-Time-Interaction-Using-Sign-Language