Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion. However, the segmentation process only considers each pixel independently, and the expansion process is difficult to achieve a favorable accuracy-speed trade-off. In this paper, we propose a Context-aware and Boundary-guided Network (CBN) to tackle these problems. In CBN, a basic text detector is firstly used to predict initial segmentation results. Then, we propose a context-aware module to enhance text kernel feature representations, which considers both global and local contexts. Finally, we introduce a boundary-guided module to expand enhanced text kernels adaptively with only the pixels on the contours, which not only obtains accurate text boundaries but also keeps high speed, especially on high-resolution output maps. In particular, with a lightweight backbone, the basic detector equipped with our proposed CBN achieves state-of-the-art results on several popular benchmarks, and our proposed CBN can be plugged into several segmentation-based methods. Code will be available on https://github.com/XiiZhao/cbn.pytorch.
翻译:最近,基于分解的方法在现场文本探测中相当流行,主要包括两个步骤:文字内核分离和扩展。但是,分解过程只独立地考虑每个像素,扩展过程很难实现一个有利的精确速度交换。在本文中,我们提议建立一个环境觉悟和边界指导网络(CBN)来解决这些问题。在CBN中,首先使用基本的文本检测器来预测初步分解结果。然后,我们提出一个符合背景的模块,以加强文本内核特征显示,既考虑全球背景,也考虑地方背景。最后,我们引入一个边界导模块,以适应性地扩展增强的文本内核,只有等离子上的等离子,不仅获得准确的文本边界,而且还保持高速,特别是在高分辨率输出地图上。特别是一个重量轻的骨架,配备了我们提议的CBN的基本探测器能够在若干流行基准上取得最新艺术结果,而且我们提议的CBNB可以插入若干基于分段的方法。 将可在 http://Xgistre/HOC/HOC/HOD. 上查到。