TY  - CONF
AU  - Leonardo Galteri
AU  - Dena Bazazian
AU  - Lorenzo Seidenari
AU  - Marco Bertini
AU  - Andrew Bagdanov
AU  - Anguelos Nicolaou
AU  - Dimosthenis Karatzas
AU  - Alberto del Bimbo
A2  - ICCV - EPIC
PY  - 2017//
TI  - Reading Text in the Wild from Compressed Images
BT  - 1st International workshop on Egocentric Perception, Interaction and Computing
N2  - 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 artifactsthat 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 significantimpact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates.
L1  - http://refbase.cvc.uab.es/files/GBS2017.pdf
UR  - http://dx.doi.org/10.1109/ICCVW.2017.283
N1  - DAG; 600.084; 600.121
ID  - Leonardo Galteri2017
ER  -