Real-Time Recognition and Translation of Kinyarwanda Sign Language into Kinyarwanda Text (2023) Mediapipe NumPy array Hands and Pose extracted key points for 22 Sign Language

This research addresses the issue of real-time translation of sign language into text (focusing on Kinyarwanda Sign Language) concentrating on twenty-two common gestures in Kinyarwanda sign language. Through extensive exploration and evaluation of various machine learning algorithms, the study identifies the most effective approach for recognizing and translating these gestures. To validate the effectiveness of the developed system, real-world Kinyarwanda sign language video data is utilized for thorough training and testing. The data set contains Hands and Pose Mediapipe extracted key points for the 22 sign language and one additional sign ("---" sign stands for not signing) saved in the NumPy array. It can be used to train the LSTM model for the classification of the 22 signs.