Paper Title
Web Application to Detect and Recognize Sign Language Alphabets Using YOLOv8

Abstract
Sign language particularly plays a crucial role in facilitating communication for individuals who are deaf or mute. A novel web application is proposed, leveraging the state-of-the-art YOLOv8 (You Only Look Once version 8) architecture for instantaneous detection and recognition of sign language alphabets in real time. The model is trained and evaluated on a comprehensive dataset, incorporating both labeled data from Robo-flow and additional images for training and testing. By employing YOLOv8, the approach demonstrates robust performance in complex environments. Moreover, the model is implemented into a web application, enabling real-time sign language alphabet detection. The application showcases accurate results, emphasizing the practicality and effectiveness of the approach in real-world scenarios. This research advances the field of sign language recognition technology and presents a web application that prioritizes user-friendliness to facilitate smooth communication. The presented findings, along with insights into the dataset, training process, and model architecture, aim to facilitate future research and development in the field of assistive communication technologies. Keywords - YOLOv8, Robo-flow, Sign Language