Real-Time AI Note Taker with Contextual Highlights

This study presents an advanced web-based intelligent note-taking system that enhances participation and productivity

in virtual meetings through the use of contextual awareness and real-time transcribing. The proposed system, Real-Time AI

Note Taker with Contextual Highlights, uses cutting-edge speech recognition and Natural Language Processing (NLP)

techniques to automatically transcribe live audio, identify crucial entities like names, actions, and deadlines, and dynamically

highlight key points of discussion. With its interactive and scalable user interface and seamless connection with video

conferencing services like Google Meet, the solution was built using the MERN stack architecture (MongoDB, Express.js,

React.js, and Node.js). The system makes use of the HuggingFace T5-small NLP model for contextual tagging and

summarization, as well as Google Speech-to-Text (STT) for precise speech recognition. The system generates clear summaries

and well-structured transcripts that can be exported, saved, and retrieved at a later time. This framework is intended for

professionals, educators, and students. It improves focus and memory, minimizes manual note-taking, and lowers cognitive load.

The suggested system encourages astute cooperation and effective information handling in both academic and professional

settings.