Year: 2025
Type: article
Abstract: Sign language is a critical communication tool for individuals with hearing and speech impairments, yet its limited understanding among the general population creates significant social and professional barriers. This research proposes a real- time hand sign detection system that leverages computer vision and deep learning to bridge this gap, enabling seamless interaction between sign language users and non-users. The system employs Convolutional Neural Networks (CNNs) for spatial feature extraction and Recurrent Neural Networks (RNNs) for temporal sequence modeling, achieving accurate recognition of hand gestures and their conversion into text or speech. By processing video input at 20–30 frames per second, the system ensures efficient real-time performance suitable for everyday use. Preliminary evaluations suggest an accuracy of 85–95% on a vocabulary of 100–200 signs, with potential scalability to larger datasets. This solution has wide-ranging applications, including education, healthcare, and customer service, fostering inclusivity and accessibility. By addressing the challenges of gesture variability and processing latency, this work advances the development of automated sign language interpretation, paving the way for more equitable communication in diverse settings. Keywords— Sign Language Recognition, Convolutional Neural Networks (CNNs), Hand Gesture Detection, Recurrent Neural Networks (RNNs), Deep Learning, Real-Time Processing.
DOI: 10.55041/ijsrem44390
Authors: Anmol Mayank, Asmika Jain
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409390291