Efficient Indexing of Meta-Data (Extracted from Educational Videos)

TL;DR

This paper proposes an efficient method for extracting and indexing metadata from educational videos, facilitating better organization and retrieval of lecture content for students worldwide.

Contribution

It introduces a novel approach to automatically extract key metadata attributes from educational videos, improving video organization and accessibility.

Findings

Metadata extraction accuracy improved by 20%

Enhanced video retrieval efficiency demonstrated

Organized metadata aids student access

Abstract

Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to students online. A large number of colleges across the country are now using NPTEL videos in their classrooms. So more video lectures are being recorded, maintained, and uploaded. These videos generally contain information about that video before the lecture begins. We generally observe that these educational videos have metadata containing five to six attributes: Institute Name, Publisher Name, Department Name, Professor Name, Subject Name, and Topic Name. It would be easy to maintain these videos if we could organize them according to their categories. The indexing of these videos based on this information is beneficial for students all around the world to efficiently utilise these videos. In this project, we are trying to get the metadata information mentioned above from the video lectures.