Real-time Scene Text Detection with Differentiable Binarization

Code

• github.com/MhLiao/DB
  OfficialIn paperpytorch★ 2,244
• github.com/PaddlePaddle/PaddleOCR
  paddle★ 72,845
• github.com/open-mmlab/mmocr
  pytorch★ 4,725
• github.com/WenmuZhou/PytorchOCR
  pytorch★ 1,515
• github.com/mindspore-lab/mindocr
  mindspore★ 298
• github.com/Mushroomcat9998/DBNet
  pytorch★ 0
• github.com/mindee/doctr
  pytorch★ 0
• github.com/jakeywu/ocr_torch
  pytorch★ 0
• github.com/18520339/dbnet-tf2
  tf★ 0
• github.com/yanan0122/dbnet-and-dbnet_pp-by-mind-spore
  mindspore★ 0

Abstract

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: https://github.com/MhLiao/DB

Tasks

Benchmark Results

Reproductions