Human Sign Language Recognition System

Effective communication poses a persistent challenge for the deaf and hard-of-hearing population, often requiring human interpreters to bridge the gap. This research introduces an innovative Human Sign Language Recognition System (HSLRS) designed to overcome these barriers through advanced computational methods. By employing deep learning frameworks, the system integrates convolutional neural networks (CNNs) for spatial feature extraction and recurrent neural networks (RNNs) for temporal sequence analysis, adeptly interpreting the nuanced gestures of sign language. Experimental results reveal high accuracy in distinguishing complex sign patterns and robust real-time recognition capabilities, validated across diverse datasets. This work advances the domains of computer vision and human-computer interaction, offering significant implications for enhancing accessibility and promoting inclusivity in educational, professional, and social contexts.