• Adarsh A S
• Akshay B
• Anantha Krishna P
• Chirag
• Suresha D

The paper uses deep learning and natural language processing techniques to create a reliable system for the automatic generation of notes from educational videos. Traditional methods of notetaking from lectures are time-consuming and often inconsistent. Early and precise transcription and summarization of lecture content can significantly improve learning efficiency and knowledge retention. The research uses a dataset of educational videos, containing various topics and spoken content. A deep learning-based model is designed and trained on this dataset to learn features that convert speech to text and extract meaningful summaries. The system is trained using labelled video data and evaluated based on its accuracy in transcribing and summarizing the content. The model achieves high accuracy in generating concise and relevant notes from video lectures. Therefore, the study demonstrates that deep learning, particularly in speech recognition and text summarization, can be effectively used for automatic note generation from educational videos, potentially aiding students and educators in academic learning and revision.

Speech-to-Text, Whisper, Text Summarization, NLP, Video Processing

"VIDEO TO NOTES GENERATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.270-274, July-2025, Available :http://www.jetir.org/papers/JETIRGX06049.pdf

"VIDEO TO NOTES GENERATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp270-274, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06049.pdf