Focal Text: an Accurate Text Detection with Focal Loss
Xiaowei Tian, Dao Wu, Rui Wang, Xiaochun Cao · 2018
Text detection in natural scene images is an important and popular task in the computer vision community. Due to slanted characters and blurred images in natural environments, it is a challenging task under active research. In this paper, we propose a Focal Text Detection Network (FTDN), which could be trained well without abundant data. FTDN is able to segment text region and simultaneously regress text box at pixel-level. Specifically, combined with focal loss, our method can balance positive/negative and easy/hard samples to achieve better performance. Compared with previous methods, FTDN achieves better performance in terms of text detection accuracy in natural scene. It outperforms the state-of-the-art methods on the standard ICDAR 2015 dataset with 80.9% F-measure.