A New Arbitrary-shaped Text Detection Network by Reinforcing Edge Features

In the detection of scene texts areas, the text instances＇edge features are processed in the same way as other features. Nevertheless, the accurate detection of adjacent text edges is crucial in the correct identification of arbitrary-shaped text regions in natural scenes. Obviously, the identification accuracy increases if edge features can be enhanced and modeled through independent branches in the network. To this end, three network modules are proposed to enhance the edge features in this paper. These modules are the shallow feature enhancement module which effectively enhances the shallow features with more edge features, the edge region detection module which decouples the original features into edge features and text features to explicitly model the edge features of the object, and the branch feature fusion module which effectively fuses these two types of features in the recognition process. After the proposed modules are added to the progressive scale expansion network (PSENet), the ablation experiments show that both the independent application and the synthetic application of these modules increase the prediction accuracy. In addition, the comparison experiments on three commonly used public datasets with ten state-of-the-art methods show that the improved edge-oriented feature reinforcing network (EFRNet) has higher F1-measure accuracy. © 2023 Science Press. All rights reserved.