Lecture notes are important for students to review and understand the key points in the class. Unfortunately, the students often miss or lose part of the lecture notes. In this paper, we design and implement an infrared sensor based system, InfraNotes, to automatically record the notes on the board by sensing and analyzing hand gestures of the lecturer. Compared with existing techniques, our system does not require special accessories with lecturers such as sensor-facilitated pens, writing surfaces or the video-taping infrastructure. Instead, it only has an infrared-sensor module on the eraser holder of black white board to capture handwritten trajectories. With a lightweight framework for handwritten trajectory processing, clear lecture notes can be generated automatically. We evaluate the quality of lecture notes by three standard character recognition techniques. The results indicate that InfraNotes is a promising solution to create clear and complete lectures to promote the education.
Some universities recorded lecture videos, therefore, a variety of researches focus on photo video analysis in order to generate class notes. One practical solution was blackboard segmentation @cite_0 . This system first analyzed video to segment written regions on blackboard. Then it exported these regions of notes in photo format. This solution was effective especially when processing lectures in the past. However, it could only generate photo, which was not a search-friendly content. Another method @cite_17 @cite_15 was to use a camera scanning whole white board with several photos. Then it stitched those images together as a panorama and eliminate background and keep enhanced notes image. This solution was practical and generated readable notes image. There was also a system @cite_21 which could recognize handwriting text from these photo by OCR. However, these two solutions could not automatically generate notes with time line in every line. In real settings, this system might miss content if it missed a single photo. The modification on notes also could not be preserved. Most significantly, the writer must not block the notes on the board, which was hard to achieve during lecture.
@cite_0 @cite_15 @cite_21 @cite_17
2013902731 2035965376 2118877014 1998609690
During the last years, digital lecture libraries and lecture video portals have become more and more popular. However, finding efficient methods for indexing multimedia still remains a challenging task. Since the text displayed in a lecture video is closely related to the lecture content, it provides a valuable source for indexing and retrieving lecture contents. In this paper, we present an approach for automatic lecture video indexing based on video OCR technology. We have developed a novel video segmenter for automated slide video structure analysis and a weighted DCT (discrete cosines transformation) based text detector. A dynamic image constrast brightness adaption serves the purpose of enhancing the text image quality to make it processible by existing common OCR software. Time-based text occurence information as well as the analyzed text content are further used for indexing. We prove the accuracy of the proposed approach by evaluation. Blackboard-style lecture videos are popular, but learning using existing video player interfaces can be challenging. Viewers cannot consume the lecture material at their own pace, and the content is also difficult to search or skim. For these reasons, some people prefer lecture notes to videos. To address these limitations, we present Visual Transcripts, a readable representation of lecture videos that combines visual information with transcript text. To generate a Visual Transcript, we first segment the visual content of a lecture into discrete visual entities that correspond to equations, figures, or lines of text. Then, we analyze the temporal correspondence between the transcript and visuals to determine how sentences relate to visual entities. Finally, we arrange the text and visuals in a linear layout based on these relationships. We compare our result with a standard video player, and a state-of-the-art interface designed specifically for blackboard-style lecture videos. User evaluation suggests that users prefer our interface for learning and that our interface is effective in helping them browse or search through lecture videos. We propose a method for segmentation of written regions on a blackboard in the lecture room using a video image. We first detect the static edges of which locations on the image are stationary. Next, we extract several rectangular regions in which these static edges are located densely. Finally, by use of fuzzy rules, the extracted rectangles are merges as contextual regions, where letters and figures in each contextual region explain each context. We apply our method to automatic production of lecture video, and archives segmentation of written regions on the blackboard in lecture rooms. Much research has been done in the past for segmenting lecture videos by detecting slide transitions. However, they do not perform well on certain kinds of videos recorded under non-stationary settings: the changes of a camera position or focus during a lecture. Since such non-stationary settings greatly affect visual properties of slides, the existing approaches utilizing global features and a global threshold, often have trouble in computing similarities between slides. In this paper, we propose a highly accurate method for lecture video segmentation by using SIFT and an adaptive threshold. By using SIFT, we can reliably match two slides whose contents are the same but are visually different. We also propose an adaptive threshold selection algorithm that detects slide transitions accurately by considering characteristics of features. Through various experiments that use real lecture videos, we show that our method provides 30 improvement in the average F1-score over other existing methods.