Efficient Scene Text Detection with Textual Attention Tower

Liang Zhang, Yufei Liu, Hang Xiao, Lu Yang, Guangming Zhu, Syed Afaq Ali Shah, Mohammed Bennamoun, Peiyi Shen

Abstract: Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multi-oriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.