As the structural information of an object can be well descripted by edge pixels, we observe that the greatest challenge for locating text edges on scene image is how to handle the edge-adhesion problem. In this paper we propose the Skeleton-cut Text Detector, which take text-specific edge cues such as a novel presentation skeleton into account to hunt text efficiently with improved recall rate. To address edge-adhesion problem, skeleton-junctions detection and elimination are performed first to cut candidate text out of the edge map. Then the candidates are verified through a two-stage classifier based on properties like concentration ratio. Finally iteratively local refinement (IRL) is applied to enhance the overlap of proposals. Experimental results on public benchmarks, ICDAR 2013 and MSRA, demonstrate that our algorithm achieves state-of-the-art performance. Moreover in severe scenarios, our proposed method shows stronger adaptability to texts by exploiting skeleton compared to conventional presentations like MSERs.
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