GestureSense: A Deep Learning-based Gesture Language Translator using VGG16 and ResNet50 Ensembles

This research study introduces a Gesture Language Translator system leveraging state-of-the-art Convolutional Neural Network (CNN) architectures, including VGG-16 and ResNet. Through extensive experimentation, the efficacy of these models in capturing the complex features of sign language gestures is evaluated in this research study. To improve translation robustness and accuracy, the Sign Language Translator system combines the capabilities of the VGG-16 and ResNet-50 models using ensemble learning approaches. Furthermore, the project incorporates data preprocessing techniques to enhance the quality and consistency of input sign language images, ensuring optimal performance of the CNN models. Transfer learning strategies are also explored to adapt pre-trained CNN models to the task of sign language recognition, thereby reducing the need for extensive labeled data. Evaluation of the Sign Language Translator system is conducted on standard benchmark dataset ASL Alphabet demonstrating promising results in real-world scenarios. Through comprehensive experimentation and analysis, this research study promotes diversity and accessibility in communication while advancing assistive technologies for people with hearing impairments.