Hand Talk AI: Sign Language Detection with Deep Learning

ABSTRACT

Communication between hearing and non- hearing individuals remains one of the most critical challenges in inclusive human interaction. Sign language serves as the primary mode of communication for the hearing- impaired community; however, a lack of sign language proficiency among the general population often results in social and professional isolation. Hand Talk AI: Sign Language Detection with Deep Learning aims to bridge this communication gap by developing an intelligent, real-time speech-to- sign language translation system. The proposed system utilizes Python-based technologies integrating speech recognition, natural language processing (NLP), and deep learning algorithms to convert spoken language into corresponding sign language gestures displayed through animated GIFs or static visual images. The system dynamically adapts to ambient noise, achieving high accuracy in speech recognition and smooth sign gesture rendering. By transforming voice input into visual signs, Hand Talk AI promotes inclusivity, accessibility, and empowerment for individuals with hearing impairments across educational, healthcare, and social environments. This innovation demonstrates the potential of deep learning and NLP integration in enhancing assistive communication technologies, fostering a more inclusive and accessible digital society.

Keywords:Sign Language Recognition, Deep Learning, Speech-to-Sign Conversion, Python, Tkinter GUI, Accessibility, NLP, Speech Recognition.