GTDNet: A Dual-branch Domain Adaption Network for Graphic-rich Text Detection
Tianci Zhang, Gang Zhou, Yajun Liu, Yangxin Liu, En Deng, Jiaqing Mo · 2023
In recent years, text detection methods based on convolutional neural networks have been extensively studied and obtained successful results in various datasets. However, many graphic- rich text instances are still difficult to detect due to their irregular appearance and easy confusion with graphics. To address this issue, we construct a dual-branch domain adaption network for graphic-rich text detection (GTDNet). Due to the coexistence of ordinary text (source domain) and graphic-rich text (target domain) in real scenarios, our dual-branch domain adaptation network can complete two tasks well at the same time. We incorporate a feature alignment module in one branch, and use an attension fusion module for fully combining two branch features. We have created a clothing printing dataset that comprises both ordinary text and graphic-rich text. Ex-tensive experiments on this dataset show that GTDNet achieves best performance on graphic-rich texts with a hardly decline performance on ordinary texts.