Real-time Scene Text Detection with Differentiable Binarization

TL;DR

This paper introduces Differentiable Binarization, a module integrated into segmentation networks for scene text detection, enabling adaptive binarization that improves accuracy and speed, achieving state-of-the-art results on multiple benchmarks.

Contribution

The paper presents a novel Differentiable Binarization module that simplifies post-processing and enhances segmentation-based scene text detection performance.

Findings

Achieves state-of-the-art detection accuracy on five benchmarks.

Significantly improves performance with lightweight backbones like ResNet-18.

Runs at 62 FPS with high detection accuracy.

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