This tool uses Deep Learning to help you draw and write with your hand and webcam. A Deep Learning model is used to try to predict whether you want to have ‘pencil up’ or ‘pencil down’.
Try it online : loicmagne.github.io/air-drawing
- This pipeline is made up of two steps: detecting the hand, and predicting the drawing. Both steps are done using Deep Learning.
- The handpose detection is performed using MediaPipe toolbox
- The drawing prediction part uses only the finger position, not the image. The input is a sequence of 2D points (actually i’m using the speed and acceleration of the finger instead of the position to make the prediction translation-invariant), and the output is a binary classification ‘pencil up’ or ‘pencil down’. I used a simple bidirectionnal LSTM architecture. I made a small dataset myself (~50 samples) which I annotated thanks to tools provided in the
python-stuff/data-wrangling/. At first I wanted to make the ‘pencil up’/’pencil down’ prediction in real-time, i.e. make the predictions at the same time the user draws. However this task was too difficult and I had poor results, which is why I’m now using bidirectionnal LSTM. You can find details of the deep learning pipeline in the jupyter-notebook in
- The application is entirely client-side. I deployed the deep learning model by converting the PyTorch model to .onnx, and then using the which is very convenient and compatible with a lot of layers.
Overall the pipeline still struggles and needs some improvement. Ideas of amelioration include :
- Having a bigger dataset, with more diverse user data.
- Process and smooth the finger signal, to be less dependent on camera quality, and to improve model generalization.