  Motivated by the success of powerful while expensive techniques to recognize
words in a holistic way, 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 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 percent f-score
the best-performing method in the last ICDAR Robust Reading Competition. Source
code of the complete end-to-end system is available at
https://github.com/lluisgomez/TextProposals
