Hasso-Plattner-Institut
  
 

Vision of the tele-TASK Project

In the center of the research work of Prof. Dr. Christoph Meinel and his team in the field of Knowledge Engineering and Web University is the tele-TASK project (tele-Teaching Anywhere Solution Kit). It was started more than 15 years ago when we began to research how Internet and web technologies can be used for supporting teaching (> tele-teaching) and learning (> e-learning). Our vision was to design an easy to use mobile system for recording and broadcasting university lectures and presentations over the Internet in order to develop and test on the one hand new tele-teaching and e-learning concepts and on the other hand to innovative portal and navigation techniques.

Demonstration des tele-TASK Systems

Our Research Directions

Over the years tele-TASK turned out to be a very fruitful project which helped us, on one side, to gain valuable experiences and a deeper understanding of e-learning and tele-teaching. On the other side, it inspires us to try our upcoming techniques in the are of Web3.0 - semantic, social, service Web -, and to make them accessible for Web-university (details). 

Some Links to the tele-TASK Portal

Here are some links to the tele-TASK portal which provides meanwhile more than 5.000 recorded telelectures: 

Buy, lease or rent tele-TASK

If you like to record and transmit your presentations over the Internet online and offline you can work with our tele-TASK recording system. Simply buy or rent tele-TASK ... 

The tele-TASK Team

  • Prof. Dr. Christoph Meinel (Head)
  • Dipl-Inf. Matthias Bauer
  • Dipl-Ing. Haojin Yang
  • Franka Grünewald, MSc.
  • Dipl.-Inf. Frank Priester (Technical Support)
  • Former Members: Volker Schillings, Tongbo Chen, Mingchao Ma, Mathias Kutzner, Bert Baumann, Long Wang, Andreas Groß, Maria Siebert, ...

tele-TASK Symposia

Scientific Publication about tele-TASK

An Improved System For Real-Time Scene Text Recognition

Haojin Yang, Cheng Wang, Xiaoyin Che, Sheng Luo, Christoph Meinel
In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR 2015), pages 657-660, Shanghai, China, 6 2015
http://dl.acm.org/ft_gateway.cfm?id=2749352&ftid=1594086&dwn=1&CFID=738916285&CFTOKEN=33723752

DOI: 10.1145/2671188.2749352

Abstract:

In this paper we showcase a system for real-time text detection and recognition. We apply deep features created by Convolutional Neural Networks (CNNs) for both text detection and word recognition task. For text detection we follow the common localization-verification scheme which already shown its excellent ability in numerous previous work. In text localization stage, textual regions are roughly detected by using a MSERs (Maximally Stable Extremal Regions) detector with high recall rate. False alarms are then eliminated by using a CNNs classifier, and remaining text regions are further grouped into words. In the word recognition stage, we developed an skeleton-based text binarization method for segmenting text from its background. A CNNs based recognizer is then applied for recognizing character. The initial experiments show the powerful ability of deep features for text classification comparing with commonly used visual features. Our current implementation demonstrates real-time performance for recognizing scene text by using a standard PC with webcam.

Keywords:

Algorithms, Demonstration, Experimentation

BibTeX file

@inproceedings{2015_Yang_ICMR,
author = { Haojin Yang, Cheng Wang, Xiaoyin Che, Sheng Luo, Christoph Meinel },
title = { An Improved System For Real-Time Scene Text Recognition },
year = { 2015 },
pages = { 657-660 },
month = { 6 },
abstract = { In this paper we showcase a system for real-time text detection and recognition. We apply deep features created by Convolutional Neural Networks (CNNs) for both text detection and word recognition task. For text detection we follow the common localization-verification scheme which already shown its excellent ability in numerous previous work. In text localization stage, textual regions are roughly detected by using a MSERs (Maximally Stable Extremal Regions) detector with high recall rate. False alarms are then eliminated by using a CNNs classifier, and remaining text regions are further grouped into words. In the word recognition stage, we developed an skeleton-based text binarization method for segmenting text from its background. A CNNs based recognizer is then applied for recognizing character. The initial experiments show the powerful ability of deep features for text classification comparing with commonly used visual features. Our current implementation demonstrates real-time performance for recognizing scene text by using a standard PC with webcam. },
keywords = { Algorithms, Demonstration, Experimentation },
url = { http://dl.acm.org/ft_gateway.cfm?id=2749352&ftid=1594086&dwn=1&CFID=738916285&CFTOKEN=33723752 },
address = { Shanghai, China },
booktitle = { Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR 2015) },
isbn = { 978-1-4503-3274-3 },
language = { English },
priority = { 0 }
}

Copyright Notice

last change: Fri, 18 Dec 2015 12:39:56 +0100

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