1.
S. Venkata Ramana
– Computer Science And Engineering, Malla Reddy Engg. College For Women, Hyderabad, Telangana, India.
2.
K. Aarati And Chetla Navyasri
– Computer Science And Engineering, Malla Reddy Engg. College For Women, Hyderabad, Telangana, India.
Abstract
Hand gesture recognition using deep learning provides a strong solution to aid disabled people, particularly those with speech and motor impairments, by allowing for effective communication and device control. This project seeks to create an intelligent system that collects and understands hand movements using camera input, utilizing advanced deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The system is designed to handle both static hand signs and dynamic gesture sequences, ensuring accurate detection in a variety of lighting conditions, backgrounds, and hand shapes. By training the model on a large dataset of hand gestures, the system may convert these motions into meaningful commands or language, allowing for easier interaction with computers, smart devices, and communication aids.
Keywords Computer vision, Deep learning & assistive technology, Disabled people, Hand gesture recognition, Image processing, Neural networks.
