Hands, translated live.
TensorFlow / Keras / OpenCV / MediaPipe / NumPy / scikit-learn / kagglehub
I started off by training a model to recognise static images of ASL signs and soon decided I’d like to evolve it further to recognise them in real time. You can practice signing letters to get graded feedback or work through a word one sign at a time. The current version focuses on the 24 static alphabet signs — J and Z, being motion signs, are left out for now.
The model tells you what it sees.
A small CNN handles recognition while a second geometric comparison looks at the shape of your hand, turning a simple classification into something you can actually learn from. So, it kinda gives you little tips about which finger to adjust to get to the desired letter. The aim is deliberately modest, to get a little better at signing the basic alphabet.
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