Tech for Inclusivity: A Universal Communication Device for Diverse Expression

Effective communication is essential for social integration, yet individuals who rely on sign language often face barriers when interacting with those unfamiliar with it. This research presents a dual-purpose system to facilitate seamless communication. The first module employs Computer Vision and Convolutional Neural Networks (CNNs) to recognize sign language gestures and convert them into text and speech in real time. The second module utilizes Speech Recognition, Natural Language Processing (NLP), and Neural Machine Translation (NMT) to transcribe spoken language and translate it into the user's preferred language. The system is accessible across various platforms, ensuring ease of use for a wide range of users. Through the integration of various technologies, this approach seeks to promote social fairness and inclusivity by bridging the communication gap between the general public and those who use sign language.