Artificial Intelligence use in Sign Recognition: a dataset in Tunisian Sign Language

With the growing impact of IoT and AI across various fields, their application in sign language recognition presents new opportunities for inclusive communication. This paper investigates different methodologies for recognizing Tunisian Sign Language by collecting and analyzing sign data. By applying advanced machine learning techniques to video-based sign recognition, this study aims to create a standardized and reliable dataset for the Tunisian signing dialect. Leveraging IoT and AI, this research enhances digital communication tools for the deaf community in Tunisia, ultimately contributing to their social integration and quality of life. The findings of our study pave the way for future advancements in AI-driven sign language recognition, fostering greater accessibility and inclusivity.E-Health, Tunisian sign language, Tunisian Dialect, Sign language recognition, Sign Language Translation, Deaf Communication, Artificial Intelligence (AI), Machine Learning, Deep Learning, Convolutional Neural Network(CNN).