Real-time Scene Text Detection Based on Global Level and Word Level Features

TL;DR

This paper introduces GWNet, a scene text detection framework that combines global and word-level features to improve accuracy and efficiency in detecting arbitrary shape text in natural scenes, outperforming existing methods.

Contribution

The paper proposes a novel GWNet framework with global and RCNN modules, enhancing adaptive performance and feature fusion for more accurate scene text detection.

Findings

Achieved high F-measures on four benchmark datasets.

Outperformed state-of-the-art detectors in accuracy.

Demonstrated effectiveness of global and word-level feature fusion.

Abstract

It is an extremely challenging task to detect arbitrary shape text in natural scenes on high accuracy and efficiency. In this paper, we propose a scene text detection framework, namely GWNet, which mainly includes two modules: Global module and RCNN module. Specifically, Global module improves the adaptive performance of the DB (Differentiable Binarization) module by adding k submodule and shift submodule. Two submodules enhance the adaptability of amplifying factor k, accelerate the convergence of models and help to produce more accurate detection results. RCNN module fuses global-level and word-level features. The word-level label is generated by obtaining the minimum axis-aligned rectangle boxes of the shrunk polygon. In the inference period, GWNet only uses global-level features to output simple polygon detections. Experiments on four benchmark datasets, including the MSRA-TD500, Total-Text, ICDAR2015 and CTW-1500, demonstrate that our GWNet outperforms the state-of-the-art detectors. Specifically, with a backbone of ResNet-50, we achieve an F-measure of 88.6% on MSRA- TD500, 87.9% on Total-Text, 89.2% on ICDAR2015 and 87.5% on CTW-1500.