Efficient Indexing of Meta-Data (Extracted from Educational Videos)
Shalika Kumbham, Abhijit Debnath, Krothapalli Sreenivasa Rao
TL;DR
This work tackles the problem of automatically extracting and indexing metadata from educational video lectures to enable efficient search and organization. It presents a pipeline that (i) collects NPTEL introduction segments, (ii) detects representative keyframes using multiple methods including a Mittal-based approach, (iii) extracts text with OCR (favoring EasyOCR), and (iv) maps text to metadata fields via fuzzy matching and SpaCy NER for Professor Names. The study demonstrates that a baseline combination of EasyOCR with ffprobe-based keyframe selection achieves the strongest accuracy across Publisher, Institute, Department, and Professor attributes on the constructed dataset. The approach enhances scalable metadata extraction for educational videos, supporting improved discoverability and indexing of lecture content. Practical impact includes better organization of MOOC and university lecture video catalogs for global learners and classroom use.
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.