EK-Net:Real-time Scene Text Detection with Expand Kernel Distance
Boyuan Zhu Fagui Liu Xi Chen Quan Tang
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.