Comparing different neural networks for accurate sign language recognition: A comprehensive study.

Sign language recognition plays a vital role in facilitating efficient communication for individuals who are deafor hard of hearing. In this research, we conduct a comparative analysis of different neural network architectures for sign language recognition. Specifically, we evaluate the performance of ANN, CNN, LSTM, CNN LSTM, Transformer and CNN Transformer models on various datasets to determine the most accurate architecture. The study involves preprocessing the datasets and establishing a standardized experimental setup for fair comparison. We train and evaluate the models using appropriate metrics to assess their accuracy. The results indicate significant variations in the performance of the neural network architectures. Our analysis provides insights into the strengths and weaknesses of each architecture in the context of sign language recognition. Based on the findings, we identify the best neural network architecture for accurate sign language recognition. The research contributes to advancing the development of robust and effective sign language recognition systems, with potential applications in communication, education, and accessibility domains. [ABSTRACT FROM AUTHOR]