Gesture Recognition in Sign Language Translation: A Deep Learning Approach

This study presents a pioneering approach in machine learning for detecting sign language, merging the capabilities of computer vision with cutting-edge deep learning methods. The primary aim of this model is the precise conversion of sign language gestures into textual or spoken forms, greatly improving communicative access for individuals with hearing impairments. This method is underpinned by advanced neural network frameworks, notably recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which are adept at processing complex visual data in real-time. Utilizing a robust and diverse dataset, the proposed system showcases its efficacy in real-time sign language recognition, demonstrating high accuracy and efficiency. The results indicate the system's adaptability in varied environmental conditions, underscoring its practical utility. This research signifies a significant advancement in assistive communication technologies, underscoring the powerful impact of sophisticated machine learning techniques in catering to the distinct communication requirements of the hearing-impaired population. By achieving this milestone, the project not only tackles a crucial accessibility issue but also establishes a foundation for subsequent breakthroughs in the realm of inclusive communication solutions.