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GIVE A HAND

Connecting to database...
Help build the world's largest dataset of hands
Ready to be trained with AI
So we can detect
the full spectrum of Sign Language.
To give everyone
an equal chance to be understood.
Participate
Take a picture of and add your hand to our database. To help accelerate the ability to detect sign language digitally.
GIVE A HAND
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Our dataset contains fully tagged images and images that are pending manual keypoint adjustments.
BROWSE HANDS
BROWSE HANDS
An open source dataset by the
American Society for Deaf Children
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#00000000
APPROVAL STATUS 1 OF 3
Image Approved
Keypoints Added
Keypoints Manually Adjusted
Give A Hand
TOTAL HANDS GATHERED
STEP — 01
Capture an image of your hand
Hold your hand in any position you want
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Error Detected

Make sure your entire hand is visible in the camera.

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About The Project

AI is on the rise, ChatGPT, MiJjourney, Stable Diffusion and other popular AI models are being released at a rapid pace.

ChatGPT is trained on a lot of textual data that's available on the internet, MidJourney and Stable diffusion are trained on billions of images sourced from the internet.

But how do we train and make better AI models from sources that are not readily available? Where data isn’t as abundant as just sourcing the internet - where we need more structural data. To get AI to detect and understand Sign Language we need better Hand Models.
There are already 3D hand model libraries out there - that make it possible to detect basic Sign Language signs - but in order to detect the full spectrum of Sign Language, we need to advance the field of hand and finger detection. It needs to be faster and more precise, to keep up with the pace of the delivery.

This project aims to aid in that process - when we have better hand models - it opens up ways to create new learning methods and real time translation tools for Sign Language. Something that is already available to many spoken languages.

That's why we are launching GiveAHand.ai - where we aim to build the world’s largest Open Source image library of hands - tagged with data - and ready to be used by AI researchers to build better hand models.

The project is all about giving greater access to this technology, so we are making the data open to everyone for both commercial and non-commercial use.

This is a platform provided by the American Society for Deaf Children. Since 1967 we have been committed to empowering diverse families with deaf and hard of hearing children and youth by embracing full access to language-rich environments through mentoring, advocacy, resources, and collaborative networks. We believe that deafness is not a disability, but language deprivation is. That’s why it’s our mission to ensure that every deaf child can learn sign language from the very start.

We welcome donations to the American Society for Deaf Children to continue and expand our efforts here.

If you have any questions about this project please reach out to us at questions@giveahand.ai

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Download Data

We are in the process of manually curating and tagging the data for the dataset.
We will be releasing the data in batches as we complete the curation process.
Please check back for updates.

The data will be shared under the CC BY-SA 4.0 license.
Below is an example of how an entry is structured.
The dataset consists of a JSON file - each entry represents a hand (Base64 image) and 3D keypoints.

{
"id": 1,
"status": "approved",
"timeStamp": "1679651016",
"key_point_0": {
"x": 40.9795761108399,
"y": 120.7547664642334,
"z": 4.1346464030311836e-8
},
"key_point_1": {
"x": 61.579208374023494,
"z": -0.014454886317253113,
"y": 121.87404155731195
},
"key_point_2": {
"x": 83.86292457580572,
"y": 100.87769508361816,
"z": -0.01947461999952793
},
"key_point_3": {
"x": 97.36550331115728,
"y": 79.44329738616943
"z": -0.023782819509506226,
},
"key_point_4": {
"x": 110.98799705505377,
"y": 61.36132478713989,
"z": -0.02791557088494301
},
"key_point_5": {
"x": 78.6369132995606
"y": 77.8624677658081,
"z": -0.003357861191034317,
},
"key_point_6": {
"x": 94.73135948181158,
"y": 57.4831080436706,
"z": -0.016267837956547737
},
"key_point_7": {
"x": 110.14338493347174,
"y": 51.49353504180908
"z": -0.027701137587428093,
},
"key_point_8": {
"x": 122.05137252807623,
"y": 51.64665699005121,
"z": -0.035186879336833954
},
"key_point_9": {
"x": 67.03835487365734,
"y": 70.86219549179077,
"z": -0.005502214189618826
},
"key_point_10": {
"x": 83.4920501708985
"y": 45.799115896224976,
"z": -0.01849016733467579,
},
"key_point_11": {
"x": 99.44948196411138
"y": 40.0106406211853,
"z": -0.03236022964119911,
},
"key_point_12": {
"x": 110.90052604675299,
"y": 43.02000045776367
"z": -0.040742699056863785,
},
"key_point_13": {
"x": 53.69373321533209,
"y": 70.1033878326416,
"z": -0.01045859232544899
},
"key_point_14": {
"x": 69.43794250488287,
"y": 40.46518921852106
"z": -0.025439821183681488,
},
"key_point_15": {
"x": 86.05518341064459,
"y": 38.10253143310547,
"z": -0.03963153436779976
},
"key_point_16": {
"x": 98.06520462036133,
"y": 42.78213500976557
"z": -0.047916531562805176,
},
"key_point_17": {
"x": 40.00000000000006,
"y": 73.54105710983276,
"z": -0.016228850930929184
},
"key_point_18": {
"x": 56.5339088439942,
"y": 59.864147901535034
"z": -0.03358835726976395,
},
"key_point_19": {
"x": 71.90470695495611,
"y": 62.45133876800537,
"z": -0.04495342820882797
},
"key_point_20": {
"z": -0.05134749412536621,
"y": 69.23082590103144,
"x": 83.26905250549322
},
"image": 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
}
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About The Project

AI is on the rise, ChatGPT, Midjourney, Stable Diffusion and other popular AI models are being released at a rapid pace.

ChatGPT is trained on a lot of textual data that's available on the internet, Midjourney and Stable diffusion are trained on billions of images sourced from the internet.

But how do we train and make better AI models from sources that are not readily available? Where data isn’t as abundant as just sourcing the internet - where we need more structural data. To get AI to detect and understand Sign Language we need better Hand Models.
There are already 3D hand model libraries out there - that make it possible to detect basic Sign Language signs - but in order to detect the full spectrum of Sign Language, we need to advance the field of hand and finger detection. It needs to be faster and more precise, to keep up with the pace of the delivery.

This project aims to aid in that process - when we have better hand models - it opens up ways to create new learning methods and real time translation tools for Sign Language. Something that is already available to many spoken languages.

That's why we are launching GiveAHand.ai - where we aim to build the world’s largest Open Source image library of hands - tagged with data - and ready to be used by AI researchers to build better hand models.

The project is all about giving greater access to this technology, so we are making the data open to everyone for both commercial and non-commercial use.

This is a platform provided by the American Society for Deaf Children. Since 1967 we have been committed to empowering diverse families with deaf and hard of hearing children and youth by embracing full access to language-rich environments through mentoring, advocacy, resources, and collaborative networks. We believe that deafness is not a disability, but language deprivation is. That’s why it’s our mission to ensure that every deaf child can learn sign language from the very start.

We welcome donations to the American Society for Deaf Children to continue and expand our efforts here.

If you have any questions about this project please reach out to us at questions@giveahand.ai