Content Based Lecture Video Retrieval Using Speech and Video Text Information

In the last decade e-lecturing has become more and more popular. The amount of lecture video data on the World Wide Web (WWW) is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This paper presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on lecture audio tracks. The OCR and ASR transcript as well as detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance and the effectiveness of proposed indexing functionalities is proven by evaluation.