  Recently, scene text detection has received significant attention due to its
wide application. However, accurate detection in complex scenes of multiple
scales, orientations, and curvature remains a challenge. Numerous detection
methods adopt the Vatti clipping (VC) algorithm for multiple-instance training
to address the issue of arbitrary-shaped text. Yet we identify several bias
results from these approaches called the "shrinked kernel". Specifically, it
refers to a decrease in accuracy resulting from an output that overly favors
the text kernel. In this paper, we propose a new approach named Expand Kernel
Network (EK-Net) with expand kernel distance to compensate for the previous
deficiency, which includes three-stages regression to complete instance
detection. Moreover, EK-Net not only realize the precise positioning of
arbitrary-shaped text, but also achieve a trade-off between performance and
speed. Evaluation results demonstrate that EK-Net achieves state-of-the-art or
competitive performance compared to other advanced methods, e.g., F-measure of
85.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.75% at 40.13 FPS on CTW1500.
