Real Time American Sign Language Detection Using Yolo-v9

TL;DR

This paper explores the application of the newly released YOLO-v9 model for real-time American Sign Language detection, demonstrating its advantages over previous models in terms of performance and efficiency.

Contribution

The study provides the first detailed analysis of YOLO-v9's effectiveness in sign language detection, highlighting improvements over earlier YOLO versions.

Findings

YOLO-v9 outperforms previous models in detection accuracy.

Real-time sign language detection is feasible with YOLO-v9.

The paper offers insights into YOLO-v9's architecture and capabilities.

Abstract

This paper focuses on real-time American Sign Language Detection. YOLO is a convolutional neural network (CNN) based model, which was first released in 2015. In recent years, it gained popularity for its real-time detection capabilities. Our study specifically targets YOLO-v9 model, released in 2024. As the model is newly introduced, not much work has been done on it, especially not in Sign Language Detection. Our paper provides deep insight on how YOLO- v9 works and better than previous model.