MS-ROCANET: MULTI-SCALE RESIDUAL ORTHOGONAL-CHANNEL ATTENTION NETWORK FOR SCENE TEXT DETECTION

Deep neural networks-based scene text detection has obtained increasing attention in recent years. However, the existing scene text detection methods cannot effectively solve the problem of unclear text features. In this paper, a Multi-scale Residual Orthogonal-Channel Attention Network (MS-ROCANet) is proposed to improve the recall and accuracy of scene text detection. Specifically, a Detail-aware FPN is designed to capture more detailed information. Then, a Shared Composite Attention Head (SCAH) consists of a Residual Orthogonal Attention Module (ROAM), and a Residual Channel Attention Block (RCAB) is proposed. It can enhance textual region features at a multi-scale level. Finally, a global context extraction module is proposed to obtain global contextual information after referring to the core idea of Transformer. Extensive experiments demonstrated that our MS-ROCANet achieved competitive results on a variety of baselines for text detection. The codes and models are available at https://github.com/ASentry/MS-ROCANet