Using Computer Vision in Helping the Deaf and Hard of Hearing Communities with Yolov5.

What would you do if you couldn’t hear anymore? What if you could only use your hands to communicate?

Simple tasks like ordering food, discussing financial matters at the bank, explaining your conditions at the hospital, or even talking to friends and family may seem daunting when they can no longer understand you.

Consider this..

To give these members of our community a greater voice, I attempted to answer this question as my Data Science boot camp capstone project at General Assembly:

Can computer vision bridge the gap for the deaf and hard of hearing by learning American Sign Language?

If ASL can accurately be interpreted through a machine learning application, even if it starts with just the alphabet, we can mark a step in providing greater accessibility and educational resources for our deaf and hard of hearing communities.

Self reflection and why I chose this project

“If it ain’t broke, don’t fix it!..”

As this was outside the realm of my job description of sales, I regretfully found myself in a hole for a long time often thinking to myself..

“but I can fix it..”

In short, I’ve buried good ideas and intentions long enough to people that wouldn’t listen to me. I had experienced a form of mental isolation before COVID-19, and it made me think about how much harder it must be for the deaf community. That is why I chose this project.

I had no idea if this would even work, but I wasn’t going to let the unknown stop me this time.

See the full capstone presentation here.

note — I genuinely hope that anyone reading this doesn’t stay stuck as long as I have, and takes the steps required to use your God given talents to its fullest!

Data and project awareness

The letter request form above was created with an introduction to my project along with instructions on how to submit voluntary sign language images with dropbox file request forms. This was distributed on social platforms to bring awareness, and to collect data.

Data warping and oversampling

Several choices in the types of augmentations were made from research that was done by Shorten, C., Khoshgoftaar. The paper can be found here.

Here is an example of what that looks like:

The data increased from 720 to 18,000 images after augmentations.


90% of the augmented images were used for training, and 10% was reserved for validation. The model was trained for 300 epochs using transfer learning with the yolov5m pretrained weights.

The final validation set successfully created new bounding boxes with labels and prediction confidence values.
As there isn’t a rise in loss values, it shows the model is not overfit, and we probably could have trained for more epochs.

A mAP@.5:.95 score of 85.27% was achieved.

To understand mAP (Mean average precision) scores, you need to understand intersection over union (IoU). Basically, it’s the area of overlap between the actual bounding box and the predicted bounding box divided by the union of the two.

It gives us a confidence level in the prediction and counts the ones over a 50% confidence threshold. The mAP@.5:.95 score is the mean of these levels between 50% and 95% in steps of 5% which shows how well the object detection model performs in this dataset.

Image Inference tests

Out of 26 letters, 4 did not receive a prediction (G, H, J, and Z)

Letters that were incorrectly predicted were:

  • “D” predicted as “F”
  • “E” predicted as “T”
  • “P“ predicted as “Q”
  • “R” predicted as “U”

Time for webcam video tests that were recorded.

Video Inference tests

Even though several images were on my hand for training, the fact that the model was able to perform pretty well on such a small dataset, and still provide good predictions without feeling slow, is very promising!

More data will only help in creating a model that could be utilized in a wide variety of new environments.

As shown on the clip, even a few letters that were partially off screen had fairly good predictions.

Probably the most surprising thing is that the letters J and Z which require movements were recognized.

Other Tests


Nearly all of the original images were right-handed. I was pleasantly surprised to see the augmentations pipeline worked here where there was a 50% chance of horizontal flips for left-handed users.


My son’s hand:

My son’s hand wasn’t used for the training set, and the model still performs pretty well here.


Multiple instances:

Although sign language is not used like the video here, it shows that multiple people can be on screen and the model will be able to distinguish more than one instance of the language.


Limitations on the current model


Many of the original pictures were taken from my phone on my hands, the distance of my hand to the camera was very close, negatively impacting inference at further distances.


New Environments:

This video is from volunteers that were not used in any of the model training. Although the model picks up a lot of the letters, the prediction confidence levels are lower, and there are more misclassifications present.


Background Interference:

This test was done to verify that different backgrounds hurt the model’s performance.



Computer vision can and should be used in marking a step in greater accessibility and educational resources for our deaf and hard of hearing communities!

The fact that the model performs pretty well using only a small dataset cannot be ignored! Even in new environments with different hands it does a pretty good job at detection. There are a few limitations that can be addressed simply with more data to train on. With a few adjustments, and a lot more data, I expect a working model that can be expanded to far more than just the ASL alphabet.

Next Steps

I believe this project is aligned with the vision of the National Association of the Deaf in bringing better accessibility and education for this underrepresented community. If I am able to bring awareness to the project, and partner with an organization like the NAD, I will be able to gather better data on the people that speak this language natively to push the project further.

The technology is still very new, and there are some limitations on models that can be deployed on mobile devices. For my final project, I primarily implemented the Data Science process to find out if this can actually work. I’m really happy with the results and I’ve already trained a smaller model that I’ll be testing for mobile deployment in the future.

Final Remarks

I’m grateful for the experience that General Assembly gave me in giving me confidence in my technical abilities and filling a personal gap of not finishing college many years back. I’ve graduated the program on Monday with some of the brightest people I’ve ever met, and I’m certain that I can finally use my gifts to make the world around me a little bit better.

What I’ve learned in this project is that computer vision can help our deaf and hard of hearing neighbors and give them the voice they deserve with technology that is available today.

If you have made it this far… Thank you… I’d love for you to share what I’m working on with your friends to hopefully make projects like this one my livelihood one day.

If you would like to get involved, or receive updates on this project, feel free to complete the embedded Google form I made below.


Yolov5 requirements

Cudnn install guide:

Install Opencv:

Roboflow augmentation process:

Heavily utilized research paper on image augmentations:

Pillow library:

Labeling Software labelImg:

Albumentations library

Link to my Github:

Special Thanks

And to my volunteers:
Nathan & Roxanne Seither
Juhee Sung-Schenck
Josh Mizraji
Lydia Kajeckas
Aidan Curley
Chris Johnson
Eric Lee

And to my General Assembly DSI-720 instructors:
Adi Bronshtein
Patrick Wales-Dinan
Kelly Slatery
Noah Christiansen
Jacob Ellena
Bradford Smith

This project would not have been possible without the time all of you invested in me. Thank you!

Data Scientist

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