• Nishchay D
• Noel Joicil Vas
• Prajwal HM
• Surya S

This comprehensive review synthesizes insights from four research studies that collectively highlight the potential of developing real-time applications to facilitate communication between individuals using sign language and those who do not. The first paper proposes an application utilizing convolutional neural networks (CNN) and Text-to-Speech translation to convert live video inputs of gestures into speech, enabling seamless real-time conversations. The second paper echoes this approach, emphasizing the importance of real-time functionality and the translation of gestures to speech via CNN and Text-to-Speech technologies. Similarly, the third paper reinforces the need for technical advancements to support real-time communication between signing and non-signing individuals, suggesting the use of CNN for gesture recognition and Text-to-Speech for conversion to speech. The fourth paper also focuses on developing a real-time application, proposing CNN and Text-to-Speech to translate gestures from live video inputs into speech, thereby facilitating effective communication. Collectively, these studies underscore the significance of integrating advanced technologies such as CNN and Text-to-Speech translators to bridge the communication gap between the speaking and non-speaking world by enabling real-time translation of sign language gestures into spoken language.

"Translating Silence: Enhancing Understanding through Sign Language Translation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.h785-h792, May-2024, Available :http://www.jetir.org/papers/JETIR2405799.pdf

"Translating Silence: Enhancing Understanding through Sign Language Translation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pph785-h792, May-2024, Available at : http://www.jetir.org/papers/JETIR2405799.pdf