Multi-orientation scene text detection leveraging background suppression

Most state-of-the-art text detection methods are devoted to horizontal texts and these methods cannot work well when encountering blurred, multi-oriented, low-resolution and small-sized texts. In this paper, we propose to localize texts from the perspective of suppressing more non-text backgrounds, in which a coarse-to-fine strategy is presented to remove non-text pixels from images. Firstly, the fully convolutional network (FCN) framework is utilized to make the coarse prediction of text labeling. Secondly, an efficient saliency measure based on background priors is employed to further suppress non-text pixels and generate fine character candidate regions. The remaining candidates of character regions composite text lines, so that the proposed method can handle multi-orientation texts in natural scene images. Two public datasets, MSRA-TD500 and ICDAR2013 are utilized to evaluate the performance of our proposed method. Experimental results show that our method achieves high recall rate and demonstrates the competitive performance.