@InProceedings{LluisGomez2018,
author="Lluis Gomez
and Mar{\c{c}}al Rusi{\~n}ol
and Dimosthenis Karatzas",
title="Cutting Sayre{\textquoteright}s Knot: Reading Scene Text without Segmentation. Application to Utility Meters",
booktitle="13th IAPR International Workshop on Document Analysis Systems",
year="2018",
pages="97--102",
optkeywords="Robust Reading",
optkeywords="End-to-end Systems",
optkeywords="CNN",
optkeywords="Utility Meters",
abstract="In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such asdirt, illumination highlights or blur.",
optnote="DAG; 600.084; 600.121; 600.129",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3102), last updated on Fri, 26 Feb 2021 14:00:13 +0100",
doi="10.1109/DAS.2018.23",
opturl="https://ieeexplore.ieee.org/abstract/document/8395178",
file=":http://refbase.cvc.uab.es/files/GRK2018.pdf:PDF"
}