Hand gesture and sign language recognition based on deep learning

Abstract

The recognition of Arabic sign language (ArSL) plays a crucial role in removing communication barriers between deaf-mute people and non-sign language speakers. In this study, we propose a dynamic model for Arabic sign language recognition using deep learning (DL) techniques. Our model utilizes a convolutional neural network (CNN) architecture to extract meaningful features from sign language (SL) images, for accurate classification of different signs. We also describe the dataset used for training and evaluating the model, which includes a collection of Arabic sign language images. After extensive experimentation and evaluation, the results prove the effectiveness of the proposed methods, achieving high recognition accuracy across multiple ArSL gestures.