论文标题
DGST：歧视者指导场景文本检测器
DGST : Discriminator Guided Scene Text detector
论文作者
论文摘要
由于其广泛的应用，场景文本检测任务在计算机视觉中引起了很大的关注。近年来，许多研究人员将语义细分的方法引入了场景文本检测的任务，并取得了令人鼓舞的结果。本文提出了一个基于条件生成对抗网络的探测器框架，以改善场景文本检测的分割效果，称为DGST（歧视者引导的场景文本检测器）。我们不是由某些现有基于语义分割的方法生成的二进制文本分数映射，而是生成一个多尺度的软文本得分映射，其中包含更多信息，以更合理地表示文本位置，并在文本提取过程中解决文本像素粘附的问题。标准数据集上的实验表明，所提出的DGST带来明显的增益，并且优于最先进的方法。具体而言，它在ICDAR 2015数据集中实现了87％的F量。
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.
