Text detection in natural scene images is an open and challenging problem due to the significant variations of the appearance of the text itself and its interaction with the context. In this paper, we present a novel text detection method combining two main ingredients: the robust extension of Stroke Width Transform (SWT) and the Deep Belief Network (DBN) based discrimination of text objects from other scene components. In the former, smoothness-based edge information is combined with gradient for generating high quality edge images, and various edge cues are exploited in Connected Component (CC) analysis on basis of SWT to eliminate inter-character and intra-character errors. In the latter, DBN is exploited for learning efficient representations discriminating character and non-character CCs, resulting in the improved detection accuracy. The proposed method is evaluated on ICDAR and SVT public datasets and achieves the state-of-the-art results, which reveal the effectiveness of the method.
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