Real-Time Scene Text Detection with Differentiable Binarization


  • Minghui Liao Huazhong University of Science and Technology
  • Zhaoyi Wan Megvii
  • Cong Yao Megvii
  • Kai Chen Shanghai Jiao Tong University
  • Xiang Bai Huazhong University of Science and Technology



Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text. However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method into bounding boxes/regions of text. In this paper, we propose a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network. Optimized along with a DB module, a segmentation network can adaptively set the thresholds for binarization, which not only simplifies the post-processing but also enhances the performance of text detection. Based on a simple segmentation network, we validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed. In particular, with a light-weight backbone, the performance improvements by DB are significant so that we can look for an ideal tradeoff between detection accuracy and efficiency. Specifically, with a backbone of ResNet-18, our detector achieves an F-measure of 82.8, running at 62 FPS, on the MSRA-TD500 dataset. Code is available at:




How to Cite

Liao, M., Wan, Z., Yao, C., Chen, K., & Bai, X. (2020). Real-Time Scene Text Detection with Differentiable Binarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11474-11481.



AAAI Technical Track: Vision