Paper Title
An Extensive Analysis of Sign Language Recognition Techniques

Abstract
An extensive analysis of Sign Language Recognition (SLR) is provided in this paper, encompassing significant subjects such as data collection, performance evaluation, usability, and the role of deep learning models. The study highlights how crucial classification accuracy is to SLR research, especially when using advanced techniques like 3D Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The work acknowledges the ongoing quest for optimal network topologies and the fact that environmental factors impact accuracy levels. It also discusses how SLR systems have evolved over time, emphasizing practical usability while becoming less reliant on complex equipment and more user-friendly. A primary concern is how to incorporate deep learning models—particularly 3D CNNs—while taking into account the ongoing challenges associated with identifying spatial relationships and body postures. Furthermore covered in the document are the translation of sign language, methodological approaches, input strategies, CNN model output formats, algorithms, accuracy issues, original research, and the inherent problems and solutions in the field of SLR. Overall, the paper offers a comprehensive and insightful overview of the evolving field of sign language recognition. Keywords - Hand Gesture Recognition, Deep Learning, Convolutional Neural Networks, 3DCNN, Text to Speech