Blackboard content classification for lecture videos

In this paper, we propose a novel approach to understand the high level semantics of instructional video by identifying mid-level features from the lecture content. The lecture content in instructional videos can be divided into text, equations and figures. In unscripted lecture video, these visual contents can be useful visual cues to understand the high level semantics. For example, it could help us achieve efficient structuring and indexing of multimedia learning material. To understand the high level semantics from the content itself is however not a trivial task. To this end, we propose visual content classification system (VCCS) for multimedia lecture videos. We propose hybrid approach by combining support vector machine (SVM) and optical character recognition (OCR) to classify visual content into figures, text and equations. The initial results show overall classification accuracy above 85 percent.