Real-Time Sign Language Recognition using Machine Learning and CNN-based EfficientNetB3 for Accessible Communication

This research analyzes the challenges faced by Sign Language Recognition (SLR) systems by evaluating the performance of EfficientNetB3, Inception-v3, and GoogLeNet models. Using a dataset of 27,000 American Sign Language (ASL) images across 27 classes, EfficientNetB3 emerged as the top performer. After fine-tuning the model with the full dataset, it achieved a test accuracy of 99.53%. We then integrated this optimized model into a real-time, user-friendly Android app for sign language recognition, offering a valuable tool for enhancing communication accessibility, particularly for the deaf and hard-of-hearing. This research work contributes to advancing SLR and promoting inclusive communication.