%0 Journal Article
%T TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild
%A Lluis Gomez
%A Dimosthenis Karatzas
%J Pattern Recognition
%D 2017
%V 70
%F Lluis Gomez2017
%O DAG; 600.084; 601.197; 600.121; 600.129
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2886), last updated on Fri, 21 Jan 2022 13:08:00 +0100
%X Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán 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 (Almazán 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.
%U https://doi.org/10.1016/j.patcog.2017.04.027
%U http://refbase.cvc.uab.es/files/GoK2017.pdf
%P 60-74