A pooling based scene text proposal technique for scene text reading in the wild
This work addresses scene text reading for computer vision applications, presenting an incremental improvement with a novel scoring function.
The paper tackles the problem of reading text in natural scenes by proposing a pooling-based technique for generating text proposals, which achieves outstanding performance in handling multi-orientation and multi-language texts and leads to competitive end-to-end system results.
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.