  Automatic reading texts in scenes has attracted increasing interest in recent
years as texts often carry rich semantic information that is useful for scene
understanding. In this paper, we propose a novel scene text proposal technique
aiming for accurate reading texts in scenes. Inspired by the pooling layer in
the deep neural network architecture, a pooling based scene text proposal
technique is developed. A novel score function is designed which exploits the
histogram of oriented gradients and is capable of ranking the proposals
according to their probabilities of being text. An end-to-end scene text
reading system has also been developed by incorporating the proposed scene text
proposal technique where false alarms elimination and words recognition are
performed simultaneously. Extensive experiments over several public datasets
show that the proposed technique can handle multi-orientation and
multi-language scene texts and obtains outstanding proposal performance. The
developed end-to-end systems also achieve very competitive scene text spotting
and reading performance.
