TextProposals: A text-specific selective search algorithm for word spotting in the wild. (October 2017)

• Record Type: Journal Article
• Authors: Gómez, Lluís; Karatzas, Dimosthenis
• Is Part Of: Pattern recognition. Volume 70(2017:Oct.)
• Page Start: 60
• Page End: 74
• Publication Date: 2017-10
• Subjects: Object proposals -- Scene text -- Perceptual organization -- Grouping; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.04.027
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We present a text specific object proposals algorithm. Our algorithm is able to reach impressive recall rates with a few thousand proposals in different standard datasets. Our method generates word proposals without an explicit character segmentation. The combination of our object proposals with existing whole-word recognizers shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Abstract: 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 .