SIGN LANGUAGE RECOGNITION

SIGN LANGUAGE RECOGNITION

C.V.Nanda Kishor, K.Venkatesham, A.Venkat Sai, A.Vikas Reddy, N.Omkar Sai Nath, Prof K.Senthil Kumar

Artificial Intellignce and Machine Learning

Malla ReddyUniversity Hyderabad

Abstract : Sign language recognition plays a vital role in bridging communication gaps between the deaf and hearing communities. Traditional approaches to sign language recognition often rely on handcrafted features and shallow learning models, limiting their performance and scalability. In recent years, deep learning techniques have shown promising results in various computer vision tasks, including sign language recognition. This paper presents a comprehensive review of deep learning methods for sign language detection. We discuss different architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, which have been successfully applied to sign language recognition tasks. Furthermore, we highlight various challenges and opportunities in this field, such as dataset availability, model complexity, and real-time performance. Finally, we provide insights into potential future directions for advancing sign language detection using deep learning techniques.