Scene text detection via decoupled feature pyramid networks

Min Liang, Jie-Bo Hou, Xiaobin Zhu, Chun Yang, Jingyan Qin

Abstract: Detecting arbitrary shape scene texts is challenging mainly due to the varied aspect ratios, curves, and scales. In this paper, we propose a novel arbitrary shape scene text detection method via Decoupled Feature Pyramid Networks (DFPN) and regression-based linking (RegLink). Our innovative DFPN decouples the width and height of feature maps generated by FPN to enhance the discriminability of features for varied aspect ratios. As quadrilateral regression results cannot directly represent curve text, we propose a simple yet effective RegLink to link pixels into text instances because pixels in the same curve text have an identical target quadrilateral. Thus, our RegLink can extend the ability of the rotated rectangles text detector for detecting curve text. Besides, we propose a Feature Scale Module to enhance the robustness of features for varied scales. In this way, our method can effectively detect scene texts in arbitrary shapes. Meanwhile, experimental results on three publicly available challenging datasets demonstrate the effectiveness of our method. The code and model of our method is available at https://github.com/lmplayer/DFPN-master .