How to precisely detect arbitrary-shaped texts in natural images has recently become a new hot topic in areas of computer vision and pattern recognition. However, the performance of most existing methods is still unsatisfactory mainly due to the intrinsic drawback of their representations for text instances. In this paper, we propose a segmentation-based method, TextPolar, for irregular scene text detection by using a novel text representation. Specifically, we predict the text center line via pixel-level segmentation and adopt polar coordinates instead of Euclidean coordinates to precisely depict the contour of text regions. Moreover, the whole detection network is also carefully designed by integrating the specific dilated convolution for multi-scale feature maps to extract rich context features. Experiments conducted on several popular scene text benchmarks, including both curved and multi-oriented text datasets, demonstrate that the proposed TextPolar obtains superior or competitive performance compared to the state of the art, e.g., 83.0% F-score for SCUT-CTW1500, 72.6% F-score for ICDAR2017-MLT, etc.

如何在自然图像中精确检测任意形状的文本已成为计算机视觉和模式识别领域的新热点。但是，大多数现有方法的性能仍然不能令人满意，这主要是由于它们对文本实例的表示所固有的缺点。在本文中，我们提出了一种基于分割的方法TextPolar，该方法通过使用新颖的文本表示来检测不规则场景的文本。具体来说，我们通过像素级分割来预测文本中心线，并采用极坐标而不是欧几里得坐标来精确描述文本区域的轮廓。此外，整个检测网络还经过精心设计，方法是将特定的扩展卷积集成到多尺度特征图中，以提取丰富的上下文特征。