Abstract
This study advances the field of real-time sign language recognition by assessing the efficacy of deep learning architectures, specifically those based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs), for the intricate task of continuous sign language interpretation. Our research highlights the RNN-based model's superior capability in processing temporal gesture sequences, indicating its suitability for predicting dynamic sign language with high accuracy. In contrast, the CNN- based model demonstrates remarkable proficiency in identifying spatial patterns within gestures, proving its utility in visually complex situations. Employing advanced data processing techniques, such as real-time pose estimation and sophisticated array manipulation for gesture classification, this work signifies a leap forward in developing AI-powered assistive technologies. The study leverages a diverse dataset to ensure the models' robust performance, underscoring deep learning's role in promoting communication inclusiveness. This scientific exploration into the capacities of deep learning methodologies underscores our commitment to leveraging cutting-edge technology for fostering more inclusive communication solutions. This research is focusing on creating automated interpretation systems for Kazakh sign language, aiming at broader inclusivity and technological innovation in communication aids.