Sign Language Translator System Using Computer Vision and LSTM Neural Networks

Computational tools that utilize computer vision and deep learning techniques, particularly Long Short-Term Memory (LSTM) neural networks, offer promising solutions to address communication barriers between hearing individuals and those with hearing impairments. This project aims to develop a system that interprets and transcribes sign language gestures into written text. In many cases, sign language is the primary means of communication for individuals with hearing loss, serving as their primary tool for interaction with the world. However, communication with non-signers remains challenging, especially when sign language is misunderstand or interpreted. This communication gap limits access to essential services such as education, employment, healthcare, and participation in daily activities. The system is designed to foster better multisensory integration by creating an environment that enables effective communication between sign language users and non-signers. The project collects and annotates a comprehensive dataset of sign language gestures, which are processed through computer vision and categorized using recurrent neural network (RNN) algorithms. Natural language processing (NLP) then converts these gestures into real-time text. This system can potentially improve the quality of life for hearing-impaired individuals by facilitating better interaction with society and enabling easier access to information and services. Implementation this system would mark a significant advancement in supporting the hearing impaired and demonstrate the power of machine learning in developing practical tools for sign language communication.