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Author (up) Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo edit   pdf
doi  openurl
  Title Reading Text in the Wild from Compressed Images Type Conference Article
  Year 2017 Publication 1st International workshop on Egocentric Perception, Interaction and Computing Abbreviated Journal  
  Volume Issue Pages  
  Abstract Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant
impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates.
  Address Venice; Italy; October 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICCV - EPIC  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GBS2017 Serial 3006  
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