Cross-Level Attention Based Adaptive Feature Alignment Network for Arbitrary-Shaped Text Detection

Over the past few years, segmentation-based text detection methods have achieved great progress in the scene text detection field. However, most of the existing methods with FPN tend to ignore the feature misalignment issues caused by semantic gap between features at different levels, leading to inaccurate segmentation prediction. Therefore, we propose an end-to-end trainable text detector to alleviate the above dilemma. Specifically, we propose an Adaptive Feature Transform Module (AFTM) to adaptively align features of different levels. Furthermore, a Cross-Level Attention (CLA) block is developed to capture the cross-level information, and selectively enhance element-wise features of multi-level feature maps. Experiments on four benchmark datasets, MSRA-TD500, Total-Text, CTW1500 and ICDAR2015, demonstrate that our proposed method has competitive performance and strong robustness.