  Scene text detection task has attracted considerable attention in computer
vision because of its wide application. In recent years, many researchers have
introduced methods of semantic segmentation into the task of scene text
detection, and achieved promising results. This paper proposes a detector
framework based on the conditional generative adversarial networks to improve
the segmentation effect of scene text detection, called DGST (Discriminator
Guided Scene Text detector). Instead of binary text score maps generated by
some existing semantic segmentation based methods, we generate a multi-scale
soft text score map with more information to represent the text position more
reasonably, and solve the problem of text pixel adhesion in the process of text
extraction. Experiments on standard datasets demonstrate that the proposed DGST
brings noticeable gain and outperforms state-of-the-art methods. Specifically,
it achieves an F-measure of 87% on ICDAR 2015 dataset.
