Hand Gesture Recognition and Text Conversion Using Convolutional Neural Networks

– Sign language is an important means of communication for people with speech disabilities, but it presents significant challenges for non-signers due to a widespread lack of interpreters and awareness. This paper explores the development of a hand sign understanding and translation system using convolutional neural networks (CNN) to bridge the communication gap between hearing and deaf communities. Our research focuses on a three-step methodology: data collection, model training and extensive evaluation. Using a custom CNN architecture, our system can detect and convert hand gestures into real-time text, providing a complete communication solution. The methodology includes a dataset specially curated for this purpose, and the training phase uses the MNIST dataset to initially calibrate the model. Our system demonstrates a remarkable 95.7% accuracy in recognizing the 26 letters of the American Sign Language (ASL) alphabet, demonstrating its potential to facilitate seamless communication between signers and non-signers. This advance highlight the promising application of deep learning methods to improve accessibility and inclusion in deaf and hearing communities.