Presentation video retrieval using automatically recovered slide and spoken text

Cooper, Matthew

doi:10.1117/12.2008433

Cited by 8 publications

(3 citation statements)

References 15 publications

(4 reference statements)

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“…Due to the fact that academic papers have always a similar structure, this system can produce an acceptable result. Cooper [13] developed a presentation-video-retrieval system using automatically recovered slides and spoken text. In this work, the slides are analyzed from the video recorded presentations.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Presentations Skills Based on Slides and Audio Features

Luzardo

Guamán

Chiluiza

et al. 2014

Proceedings of the 2014 ACM Workshop on Multimodal Learning Analytics Workshop and Grand Challenge

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This paper proposes a simple estimation of the quality of student oral presentations. It is based on the study and analysis of features extracted from the audio and digital slides of 448 presentations. The main goal of this work is to automatically predict the values assigned by professors to different criteria in a presentation evaluation rubric. Machine Learning methods were used to create several models that classify students in two clusters: high and low performers. The models created from slide features were accurate up to 65%. The most relevant features for the slide-base models were: number of words, images, and tables, and the maximum font size. The audio-based models reached up to 69% of accuracy, with pitch and filled pauses related features being the most significant. The relatively high degrees of accuracy obtained with these very simple features encourage the development of automatic estimation tools for improving presentation skills.

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Section: Related Workmentioning

confidence: 99%

Estimation of Presentations Skills Based on Slides and Audio Features

Luzardo

Guamán

Chiluiza

et al. 2014

Proceedings of the 2014 ACM Workshop on Multimodal Learning Analytics Workshop and Grand Challenge

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“…Speech is yet another important signal we could use for retrieval; some browser vendors such as Google Chrome offer live speech to text for web applications through the Web Speech API; while the quality is not yet good enough for live transcripts, the text will provide enough words for retrieval purposes, especially if combined with words recognized on screen by the OCR engine, as was shown in [1] and [3].…”

Section: Discussionmentioning

confidence: 99%

Searching Live Meeting Documents "Show me the Action"

Denoue

Carter

Cooper

2015

Proceedings of the 2015 ACM Symposium on Document Engineering

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Live meeting documents require different techniques for effectively retrieving important pieces of information. During live meetings, people share web sites, edit presentation slides, and share code editors. A simple approach is to index with Optical Character Recognition (OCR) the video frames, or key-frames, being shared and let user retrieve them. Here we show that a more useful approach is to look at what actions users take inside the live document streams. Based on observations of real meetings, we focus on two important signals: text editing and mouse cursor motion. We describe the detection of text and cursor motion, their implementation in our WebRTC (Web Real-Time Communication)-based system, and how users are better able to search live documents during a meeting based on these extracted actions.

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“…The author has established an IR system which determines the sub words units into the valid word. Matthew Cooper [5] has analyzed the results from both the OCR and ASR algorithm. He has presents that there is mega aberration in texts retrieved from ASR and OCR method.…”

Section: Literature Surveymentioning

confidence: 99%

Pacify Based Video Retrieval System

.¹

2015

IJRET

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Recently, Video is becoming a catholic medium for e-learning. As per the popularity of online video information over the World Wide Web (WWW) is mostly dependent on user-assigned tags or specification, which is the system by which we can access such videos. However, this system have limitations for retrieval and frequently we want access to the content (pacify) of the video itself is directly matched against a user's query except manually assigned tags or specifications. In e-lecturing videos it contains visual and aural medium: slides of presentation and speech. in this system, we are going to retrieve the text from the videos automatically. To abstract visible information, we apply video content analysis to detect slides and optical character recognition to obtain their text. We abstract textual metadata by applying video Optical Character Recognition (OCR) technology on keyframes and Automatic Speech Recognition (ASR) on lecture audio. The ASR and OCR translate and discover slide text line types are accept for keywords abstraction, in which video and fragment-level keywords are abstracted for video searching on the basis of contents.

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Presentation video retrieval using automatically recovered slide and spoken text

Cited by 8 publications

References 15 publications

Estimation of Presentations Skills Based on Slides and Audio Features

Estimation of Presentations Skills Based on Slides and Audio Features

Searching Live Meeting Documents "Show me the Action"

Pacify Based Video Retrieval System

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