Sign Language Generation and Detection using Deep Learning

Deep Learning

This study focuses on developing innovative two-way communication systems that allow for seamless interactions between people who use sign language and those who do not. The system uses deep learning techniques, particularly annoying neural networks (CNNs), to enable real-time translation between text, audio and sign language. The main purpose of this project is to bridge communication gaps and to provide access, more efficient and integrated daily interactions for the deaf and hearing at community hearings. In sign language, the difficulty is communicating with people who are not used to it. While existing solutions exist, such as sign language interpreters and mobile applications, they are often unrealistic, expensive or unavailable in real time. Many current technologies offer disposable translations from sign language to text or vice versa, but do not provide integrated two-way communication systems. The aim of our study is to overcome these limitations by designing a comprehensive solution that allows for smooth interaction between sign language users and non-users in real-world scenarios. The recognition module uses CNNs to recognize hand gestures related to sign language and convert them into English text. This function allows those who communicate effectively with non signed voice users by using sign language to convert gestures into real-time readable text. The CNN model is trained with a variety of data records with sign language gestures to ensure high accuracy and robustness in a variety of lighting conditions, hand positions and user variations. Converts voice audio inputs and their corresponding sign language gestures. This feature is particularly advantageous for non-essential voice users who want to communicate with people who rely on sign language. By using deep learning models that include NLP techniques (natural language processing), the system processes input text or language and generates accurate representations of visual sign language. The integration of speech recognition provides even greater accessibility, allowing you to convert spoken language into sign language without the need for manual input. A variety of environments, including educational institutions, employment, medical facilities, and public service centers. By using deep learning and computer vision technology, our research contributes to continuous efforts to improve the inclusion and accessibility of hearing impairment and hearing loss.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.