Sign language recognition through a machine learning approach

Abstract

Sign language recognition is a critical area of research for improving communication and accessibility for the deaf community. With advances in machine learning, automatic recognition of sign language gestures has become possible. This research paper provides an overview of machine learning approaches used in sign language recognition, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs). The challenges in sign language recognition, such as variation in gestures, background clutter, occlusion, and lighting conditions, are also discussed. Additionally, this paper highlights potential future research directions, such as multi-modal sign language recognition and transfer learning.