Enhancing Scene Text Detection via Fused Semantic Segmentation Network with Attention

Abstract

Scene text detection (STD) in natural images is still challenging since text objects exhibit vast diversity in fonts, scales and orientations. Deep learning based state-of-the-art STD methods are promising such as PixelLink which has achieved 85% accuracy on ICDAR 2015 benchmark. Our preliminary experimental results with PixelLink have shown that its detection errors come mainly from two aspects: failing to detect the small scale and ambiguous text objects. In this paper, following the powerful PixelLink framework, we try to improve the STD performance via delicately designing a new fused semantic segmentation network with attention. Specifically, an inception module is carefully designed to extract multi-scale receptive field features aiming at enhancing feature representation. Besides, a hierarchical feature fusion module is cascaded with the inception module to capture multi-level inception features to obtain more semantic information. At last, to suppress background disturbance and better locate the text objects, an attention module is developed to learn a probability heat map of texts which helps accurately infer the texts even for ambiguous texts. Experimental results on three public benchmarks demonstrate the effectiveness of our proposed method compared with the state-of-the-arts. We note that the highest F-measure on ICADR 2015, ICADR 2013 and MSRA-TD500 has been obtained for our proposed method but the higher computational cost is required.