BDFPN: Bi-Direction Feature Pyramid Network for Scene Text Detection

Scene text detection in the natural environment is widely used in real-world applications, ranging from autonomous driving, image search and assistance for the blind. However, a vast of the existing methods have limited ability to detect text instances in challenging scenes such as texts with low contrast or blur. To address the problem, we propose a novel Bi-Direction Feature Pyramid Network (BDFPN), which draws inspiration from the two-way visual information processing mechanism of human beings. Specifically, the bottom-up path is data-driven for fine details and the top-down path is task-driven for obtaining semantic information. In the top-down path, the Feature Alignment Module (FAM) is proposed to narrow the semantic differences that exist in features of adjacent levels. To combine features from two paths, we propose a novel fusion strategy named Attention Fusion Module (AFM). We conduct extensive experiments on ICDAR2015, Totaltext and MSRA-TD500 to demonstrate the effectiveness and robustness of BDFPN.