• Ningthoujam Justwant Singh

This paper presents a real-time hand sign recognition system using a machine learning architecture. The project introduces an implementation of computer vision for Hand gesture recognition. Hand gesture recognition machine received fantastic attention in the current few years due to its manifoldness programs and the potential to interact with system effectively through human laptop interplay. Real-time hand sign recognition using machine learning architecture is a technology that can detect and recognize hand signs in real-time using machine learning algorithms. This technology is important for people with hearing or speech disabilities who use sign language as their primary means of communication. The machine learning algorithms can learn to recognize the different hand signs and gestures, allowing the system to accurately translate them into text or voice commands. This technology has the potential to improve communication and accessibility for people with disabilities and enhance human-machine interaction in various applications. The system is trained with some hand sign recognition which are (1, 2, 3, 4, 5, 6, 7, 8, 9, 0) in real-time.

gesture, mediapipe, hands

"Realtime hand sign recognition using machine learning architecture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.c817-c820, July-2023, Available :http://www.jetir.org/papers/JETIR2307286.pdf

"Realtime hand sign recognition using machine learning architecture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppc817-c820, July-2023, Available at : http://www.jetir.org/papers/JETIR2307286.pdf