@Article{LluisGomez2017,
author="Lluis Gomez
and Dimosthenis Karatzas",
title="TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild",
journal="Pattern Recognition",
year="2017",
volume="70",
pages="60--74",
abstract="Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almaz{\'a}n et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almaz{\'a}n et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10\% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals.",
optnote="DAG; 600.084; 601.197; 600.121; 600.129",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2886), last updated on Fri, 21 Jan 2022 13:08:00 +0100",
opturl="https://doi.org/10.1016/j.patcog.2017.04.027",
file=":http://refbase.cvc.uab.es/files/GoK2017.pdf:PDF"
}