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The usage of non-scripted lecture videos as a part of learning material is becoming an everyday activity in most of higher education institutions due to the growing interest in flexible and blended education. Generally these videos are delivered as part of Learning Objects (LO) through various Learning Management Systems (LMS). Currently creating these video learning objects (VLO) is a cumbersome...
In this paper, we address the issues pertaining to segmentation and recognition of cursive handwritten text from chalkboard lecture videos. Recognizing handwritten text is a challenging problem in instructor-led lecture video. The task gets even tougher with varying handwriting styles and blackboard type. Unlike handwritten text on whiteboard and electronic boards, chalkboard represents serious challenges...
In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The keywords undergo a series of refinement process...
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...
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