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Evaluation Framework for Video OCR

  • Padmanabhan Soundararajan
  • Matthew Boonstra
  • Vasant Manohar
  • Valentina Korzhova
  • Dmitry Goldgof
  • Rangachar Kasturi
  • Shubha Prasad
  • Harish Raju
  • Rachel Bowers
  • John Garofolo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

In this work, we present a recently developed evaluation framework for video OCR specifically for English Text but could well be generalized for other languages as well. Earlier works include the development of an evaluation strategy for text detection and tracking in video, this work is a natural extension. We sucessfully port and use the ASR metrics used in the speech community here in the video domain. Further, we also show results on a small pilot corpus which involves 25 clips. Results obtained are promising and we believe that this is a good baseline and will encourage future participation in such evaluations.

Keywords

Evaluation Framework Text Region Word Boundary Word Error Rate Broadcast News 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Padmanabhan Soundararajan
    • 1
  • Matthew Boonstra
    • 1
  • Vasant Manohar
    • 1
  • Valentina Korzhova
    • 1
  • Dmitry Goldgof
    • 1
  • Rangachar Kasturi
    • 1
  • Shubha Prasad
    • 2
  • Harish Raju
    • 2
  • Rachel Bowers
    • 3
  • John Garofolo
    • 3
  1. 1.Computer Science and EngineeringUniversity of South FloridaTampaUSA
  2. 2.VideoMining CorporationUSA
  3. 3.Information Technology Lab – Information Access Division, Speech GroupNational Institute of Standards and Technology (NIST) 

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