Inspired by deep convolution segmentation algorithms, scene text detectors
break the performance ceiling of datasets steadily. However, these methods
often encounter threshold selection bottlenecks and have poor performance on
text instances with extreme aspect ratios. In this paper, we propose to
automatically learn the discriminate segmentation threshold, which
distinguishes text pixels from background pixels for segmentation-based scene
text detectors and then further reduces the time-consuming manual parameter
adjustment. Besides, we design a Global-information Enhanced Feature Pyramid
Network (GE-FPN) for capturing text instances with macro size and extreme
aspect ratios. Following the GE-FPN, we introduce a cascade optimization
structure to further refine the text instances. Finally, together with the
proposed threshold learning strategy and text detection structure, we design an
Adaptive Segmentation Network (ASNet) for scene text detection. Extensive
experiments are carried out to demonstrate that the proposed ASNet can achieve
the state-of-the-art performance on four text detection benchmarks, i.e., ICDAR
2015, MSRA-TD500, ICDAR 2017 MLT and CTW1500. The ablation experiments also
verify the effectiveness of our contributions.

基于深度卷积分割算法的场景文本检测器通过学习判别性分割阈值和设计全局信息增强特征金字塔网络，实现了对具有极端长宽比的文本实例的有效检测和自适应分割，为场景文本检测提供了最新的性能表现。