CVSI 2017



ICDAR2017 Competition on Video Script Identification

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Important dates:

  • Feb 1, 2017 : Website online
  • Feb 1, 2017 : Registration Open
  • Feb 20, 2017: Sample dataset available
  • March 15, March 24, April 14
    April 21, 2017: Training dataset available
  • March 25, April 3, April 21
    April 28 2017: Validation dataset available
  • March 31, April 15, May 1
    June 30, 2017: Registration closes
  • April 5, April 18, May 5,
    June 30, 2017: Submission of systems
  • April 7, April 20, May 10,
    May 20, 2017: Test dataset available
  • April 10, April 24, May 17,
    June 30, 2017: Submission of results

Latest News

  • Feb 1, 2017 : Website online, registration open
  • Feb 20, 2017 : Sample Dataset available
  • March 15, 2017 : Deadline extension
  • March 31, 2017 : Following the extension of the ICDAR2017 full paper submission deadline, the training dataset will now be available on 14th April 2017 to the registered participants
  • April 21, 2017 : The training dataset is now available to the registered participants
  • April 30, 2017 : The validation dataset is now available to the registered participants
  • May 29, 2017 : The test dataset is available after system submission
  • May 29, 2017 : Competition deadline extend
  • >

Organizers

1. Dr. Nabin Sharma,
Research Associate, School of Software, University of Technology Sydney, Australia.

2. Mr. Rabi Sharma,
Researcher, Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, India.

3. Dr. Ranju Mandal,
Research Assistant, School of ICT, Griffith University, Australia.

4. Prof. Michael Blumenstein,
Professor and Head of School of Software, University of Technology Sydney, Australia.

5. Prof. Umapada Pal,
Professor and Head of Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, India





Acknowledgement

The organizers would like to acknowledge the help and support provide by
1. Mr. Rishav Chatterjee,
2. Ms. Jinmeng Piao,
3. Mr. Kohei Hatsuzawa, and
4. Ms. Darin Suriyapornchaikul
for data collection and dataset preparation.