Geometric Feature-Based Machine Learning for Efficient Hand Sign Gesture Recognition

Abstract

Hand Gesture Recognition (HGR) is emerging as a vital tool in enhancing communication, particularly for individuals who are deaf or hard of hearing. Despite its potential, widespread use of sign language remains constrained by limited understanding among the general public. Previous research has explored various models to bridge this communication gap. However, deploying complex deep learning algorithms on low-power, cost-effective embedded devices presents significant challenges due to constraints on memory and energy resources. In this research, we introduce a new approach by leveraging lightweight machine learning algorithms for real-time hand sign recognition, utilizing novel geometrical features derived from hand landmarks. Our approach optimizes computational efficiency without compromising accuracy, making it suitable for resource-limited devices. The proposed model not only achieves higher accuracy compared to existing methods but also demonstrates that a focus on feature design can outperform more complex deep learning architectures, thereby offering a promising solution for real-time, accessible HGR applications.