@InProceedings{RaulGomez2019,
author="Raul Gomez
and Ali Furkan Biten
and Lluis Gomez
and Jaume Gibert
and Mar{\c{c}}al Rusi{\~n}ol
and Dimosthenis Karatzas",
title="Selective Style Transfer for Text",
booktitle="15th International Conference on Document Analysis and Recognition",
year="2019",
pages="805--812",
optkeywords="transfer",
optkeywords="text style transfer",
optkeywords="data augmentation",
optkeywords="scene text detection",
abstract="This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which meanstransferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.",
optnote="DAG; 600.129; 600.135; 601.338; 601.310; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3265), last updated on Tue, 23 Feb 2021 12:55:41 +0100",
doi="10.1109/ICDAR.2019.00134",
opturl="https://ieeexplore.ieee.org/abstract/document/8977953",
file=":http://refbase.cvc.uab.es/files/GBG2019.pdf:PDF"
}