Romanian Sign Language and Mime-Gesture Recognition

paper presents a comprehensive approach to Romanian Sign Language (RSL) recognition using machine learning techniques. The primary focus is on developing and evaluating a robust model capable of accurately classifying hand and mime gestures representative of RSL and converting it into speech through an application. Utilizing a dataset of hand landmarks captured and stored in CSV format, the study outlines the preprocessing steps, model training, and performance evaluation. Key components of the methodology include data preparation, model training, performance evaluation and model optimization. The results demonstrate the feasibility of using machine learning for RSL recognition, achieving promising accuracy rates. The study concludes with a discussion on potential applications and future enhancements, including real-time gesture recognition and expanding the dataset for improved generalization. This work contributes to the broader effort of making sign language more accessible through technology, particularly for the Romanian-speaking deaf and hard-of-hearing community.