Comparing presentation summaries: slides vs. reading vs. listening
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An automated end-to-end lecture capture and broadcasting system
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
Analysis, indexing and visualization of presentation videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Development and evaluation of indexed captioned searchable videos for STEM coursework
Proceedings of the 43rd ACM technical symposium on Computer Science Education
PEDIVHANDI: multimodal indexation and retrieval system for lecture videos
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Bag of subjects: lecture videos multimodal indexing
Proceedings of the 2013 ACM symposium on Document engineering
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We propose a fully automatic method for summarizing and indexing unstructured presentation videos based on text extracted from the projected slides. We use changes of text in the slides as a means to segment the video into semantic shots. Unlike precedent approaches, our method does not depend on availability of the electronic source of the slides, but rather extracts and recognizes the text directly from the video. Once text regions are detected within keyframes, a novel binarization algorithm, Local Adaptive Otsu (LOA), is employed to deal with the low quality of video scene text, before feeding the regions to the open source Tesseract OCR engine for recognition. We tested our system on a corpus of 8 presentation videos for a total of 1 hour and 45 minutes, achieving 0.5343 Precision and 0.7446 Recall Character recognition rates, and 0.4947 Precision and 0.6651 Recall Word recognition rates. Besides being used for multimedia documents, topic indexing, and cross referencing, our system can be integrated into summarization and presentation tools such as the VAST MultiMedia Browser [1].