@inbook{f1400ea17f8a4fdf84bd8befd8a7ee62,
title = "Reading Text in the Wild from Compressed Images",
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.",
author = "Leonardo Galteri and Dena Bazazian and Lorenzo Seidenari and Marco Bertini and Bagdanov, {Andrew D.} and Anguelos Nicolaou and Dimosthenis Karatzas and {Del Bimbo}, Alberto",
note = "Funding Information: This work was supported by the CERCA Programme of the Generalitat de Catalunya, the research project TIN2014-52072-P. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. Publisher Copyright: {\textcopyright} 2017 IEEE.",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/ICCVW.2017.283",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2399--2407",
booktitle = "Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017",
address = "United States",
}