Year: 2025
Type: article
Abstract: Sign language serves as a vital communication medium for individuals with hearing and speech impairments. Recent advances in computer vision and deep learning, especially Convolutional Neural Networks (CNNs), have paved the way for more accurate and real-time gesture recognition systems. This paper presents a CNN-based model for recognizing hand gestures corresponding to American Sign Language (ASL). The proposed system utilizes image preprocessing techniques, a custom CNN architecture, and publicly available datasets to train and validate the model. Experimental results show that our system achieves an accuracy of over 95%, demonstrating its effectiveness in translating sign language into textual format.
Author: Prof. Dayanand Argade
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410895502