Sign Language (ASL) Detection System using MobileNet, TensorFlow v2, Python, Flask, OpenCv2, Web Speech API
This gif shows ASL sign detetcion and recognition of the class which is written as interpreted text.
To hear the text-to-speech, navigate to Live_ASL_detection_TTS.mp4
First we create the virtual environment and clone the repository into it. Then, we install all dependencies before running the application.
python3 -m venv /path/to/venv
cd /path/to/venv
git clone https://github.com/priyanka-maz sign-language-detection
source bin/activate
cd sign-language-detection
python3 -m pip install -r requirements.txt
python3 app.py-
Click on 'Start' on the homepage to begin sign language detection on your video feed
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Permissions for video feed usage must be set to 'Allow'
-
Write letters in American Sign Language to form words or phrases which show up on the console on the right
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Click on 'Play' button below the console to convert your sentences from text-to-speech for seamless communication
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For non-ASL users, you can refer to the English to ASL converter on the homepage
-
Follow 'Tips' under console for further instructions
- This website was developed using Python, Flask, HTML, CSS & Javascript.
- Users who wish to converse in ASL, may use this detection system to convert the interpreted text to speech for communicating without bounds.
- Users who aren't equipped with ASL may use the English to ASL converter to learn relevant symbols.
- The voice for TTS technology may be changed in settings.
- Developers may check the raw footage opened using SocketIO and the prediction on top of that OpenCV feed.
- Text-to-speech translation uses
SpeechSynthesisUtterance()of the Web Speech API. TensorFlow 2.0andKeraslibrary is used in the development of our model. The final model used for testing and deployment isMobileNet.- Other machine learning techniques employed are:
- Data Augmentation
- Transfer Learning
- Validation Set for Accuracy metric
- Early Stopping
- Nadam Optimizer
- Exponential Decay Learning Rate
- Categorical Cross Entropy Loss
.
├── app.py.............................Runs the Flask application and deploys all webpages
│ as well as calls necessary computational functions
├── datasets...........................Contains tar.gz file of the dataset and
│ some necessary statistics for the dataset
├── LICENSE............................MIT LICENSE
├── models.............................TFLITE version models of MobileNet and EfficientNet
│ ├── model_efficientnet_v2s.tflite
│ └── model_mobilenet_v2.tflite
├── README.md
├── requirements.txt
├── screenshots........................Contains necessary visualizations about the
│ model performance, data and website
├── static.............................Contains all js, css files and images used in the website
│ ├── connection.js
│ ├── opencv.js
│ └── style.css
├── templates..........................Contains all HTML templates deployed in the website
│ ├── about.html
│ ├── index.html.....................HTML template of the page where sign detection occurs
│ ├── landing.html
│ └── layout.html
├── train.py...........................Used to train the entire model, contains all
│ ML techniques mentioned above
├── webcam_detect.py...................Loads the model and predicts the class from the
│ frame input given as softmax probability which is sent
│ back to the client's end
└── webcam.py..........................Used for testing ASL Sign Detection and preiction locally
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