Reconnaissance Automatique des Langues des Signes : Une Approche Hybridée CNN-LSTM Basée sur Mediapipe
This addresses communication barriers for deaf communities in accessing services like healthcare and education, though it is incremental as it builds on existing CNN-LSTM and Mediapipe methods.
The study tackled automatic sign language recognition by proposing a hybrid CNN-LSTM system using Mediapipe for keypoint extraction, achieving an average accuracy of 92% for distinct gestures like 'Hello' and 'Thank you'.
Sign languages play a crucial role in the communication of deaf communities, but they are often marginalized, limiting access to essential services such as healthcare and education. This study proposes an automatic sign language recognition system based on a hybrid CNN-LSTM architecture, using Mediapipe for gesture keypoint extraction. Developed with Python, TensorFlow and Streamlit, the system provides real-time gesture translation. The results show an average accuracy of 92\%, with very good performance for distinct gestures such as ``Hello'' and ``Thank you''. However, some confusions remain for visually similar gestures, such as ``Call'' and ``Yes''. This work opens up interesting perspectives for applications in various fields such as healthcare, education and public services.