Easy_sign is an open source russian sign language recognition project that uses small CPU model for predictions and is designed for easy deployment via Streamlit.
Easy_sign uses a machine learning model to recognize Russian sign language gestures. The model was trained on ~180,000 examples of Russian sign language gestures. Approximately 20,000 of which are taken from the Slovo dataset. The model recognizes 1598 Russian sign language gestures and can process 3-3.5 gestures per second on an Intel(R) Core(TM) i5-6600 CPU @3.30GHz. The list of recognized gestures is available in the file RSL_class_list.txt.
To learn more about the project - visit the link to our article on habr.
conda create --name fleury-env python=3.10
conda activate fleury-env
pip install -r requirements.txt
streamlit run app.py
The PINCODE team has released a training simulator based on easy_sign to study Russian sign language. Habr (Russian), github
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
