Abstract
Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or user-specified text regions regardless of font, script, language or shape is not available. Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting. To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet is a conditional adversarial generative network (cGAN) with an auxiliary mask. The introduced auxiliary mask not only makes the cGAN a generic text eraser, but also enables stable training and early convergence on a challenging large-scale synthetic dataset, initially proposed for text detection in real scenes. What's more, MTRNet achieves state-of-the-art results on several real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous state-of-the-art methods trained directly on these datasets.
Abstract (translated)
本文提出了具有规则形状和布局的单语脚本的文本删除算法。但是，据我们所知，没有一种通用的文本删除方法可以删除所有或用户指定的文本区域，而不管字体、脚本、语言或形状如何。为真实场景开发这种通用的文本擦除器是一项具有挑战性的任务，因为它继承了多语言和曲线文本检测和绘制的所有挑战。为了填补这一空白，我们提出了一种基于屏蔽的文本删除网络（MTRNET）。MTRNET是一个有条件的对抗性生成网络（CGAN），带有一个辅助屏蔽。引入的辅助掩模不仅使cgan成为一种通用的文本擦除器，而且能够在一个具有挑战性的大规模合成数据集上进行稳定的训练和早期的收敛，最初提出用于真实场景中的文本检测。此外，MTRNET在多个现实数据集（包括ICDAR 2013、ICDAR 2017 MLT和CTW1500）上获得了最先进的结果，但没有对这些数据进行明确的培训，优于以前直接在这些数据集上培训的最先进方法。
URL
https://arxiv.org/abs/1903.04092
