  Segmentation-based scene text detection methods have been widely adopted for
arbitrary-shaped text detection recently, since they make accurate pixel-level
predictions on curved text instances and can 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 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 performance in both speed and accuracy, without complex label
assignments or repeated feature aggregations. 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.
