Curved scene text detection via transverse and longitudinal sequence connection

Abstract Curved text detection is a difficult problem that has not been addressed sufficiently. To highlight the difficulties in reading curved text in a real environment, we constructed a curved text dataset called CTW1500, which includes over 10,000 text annotations in 1500 images, and used it to formulate a polygon-based curved text detector that can detect curved text without using an empirical combination. With the seamless integration of recurrent transverse and longitudinal offset connection, our method explores context information instead of predicting points independently, resulting in smoother and more accurate detection. Our approach is designed as a universal method, meaning it can be trained using rectangular or quadrilateral bounding boxes, requiring no extra effort. Experimental results on the CTW1500 dataset and Total-text demonstrated that our method with only a light backbone can outperform state-of-the-art methods by a large margin. Our method also achieved state-of-the-art performance on the MSRA-TD500 dataset, demonstrating its promising generalization ability. Code, datasets, and label-tool are available at https://github.com/Yuliang-Liu/Curve-Text-Detector .