RSCA: Real-Time Segmentation-Based Context-Aware Scene Text Detection

Jiachen Li, Yuan Lin, Rongrong Liu, Chiu Man Ho, Humphrey Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 2349-2358

Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, because they make accurate pixel-level predictions on curved text instances and facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models are unable to learn the shapes of curved texts and often require complex label assignments or repeated feature aggregations for more accurate detection. In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for arbitrary-shaped scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic Text-Spine Labeling, which model local spatial transformation and simplify label assignments separately. Based on these strategies, RSCA achieves state-of-the-art performances without complex label assignments or repeated feature aggregations in a real-time inference speed. We conduct extensive experiments on multiple benchmarks to validate the effectiveness of our method. RSCA-640 reaches 83.9% F-measure at 48.3 FPS on CTW1500 dataset.