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DBNet

Real-time Scene Text Detection with Differentiable Binarization

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.

Results and models

SynthText

Method Backbone Training set #iters Download
DBNet_r18 ResNet18 SynthText 100,000 model | log

ICDAR2015

Method Backbone Pretrained Model Training set Test set #epochs Test size Precision Recall Hmean Download
DBNet_r18 ResNet18 - ICDAR2015 Train ICDAR2015 Test 1200 736 0.8853 0.7583 0.8169 model | log
DBNet_r50 ResNet50 - ICDAR2015 Train ICDAR2015 Test 1200 1024 0.8744 0.8276 0.8504 model | log
DBNet_r50dcn ResNet50-DCN Synthtext ICDAR2015 Train ICDAR2015 Test 1200 1024 0.8784 0.8315 0.8543 model | log
DBNet_r50-oclip ResNet50-oCLIP - ICDAR2015 Train ICDAR2015 Test 1200 1024 0.9052 0.8272 0.8644 model | log

Total Text

Method Backbone Pretrained Model Training set Test set #epochs Test size Precision Recall Hmean Download
DBNet_r18 ResNet18 - Totaltext Train Totaltext Test 1200 736 0.8640 0.7770 0.8182 model | log

Citation

@article{Liao_Wan_Yao_Chen_Bai_2020,
    title={Real-Time Scene Text Detection with Differentiable Binarization},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
    year={2020},
    pages={11474-11481}}