TextPolar: irregular scene text detection using polar representation

摘要

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.