A dual branch graphic text detection network based on progressive Domain adaptation

In recent years, scene text detection methods have been widely studied and have achieved good results on various common text datasets. However, graphic text commonly found in movie posters and clothing prints tends to have irregular appearances, leading to a decline in the performance of existing text detection methods. To address this issue, we propose a dual-branch graphic text detection network based on progressive domain adaptation (GTDNet++). We achieve feature alignment between the source and target domains through progressive domain adaptation in one branch. Additionally, we use an attention fusion module to fully combine the features from both branches, further reducing domain shift and addressing the issue of source domain forgetting. Extensive experiments on this dataset demonstrate that GTDNet++ achieves state-of-the-art performance on graphic text and mixed text scenarios.