Character Can Speak Directly: An End-to-End Character Region Excavation Network for Scene Text Spotting

End-to-end scene text spotting methods have garnered significant research attention due to their promising results. However, most existing approaches are not well suited for real-world applications because of their inherently complex pipelines. In this paper, we propose an end-to-end Character Region Excavation Network (CRENet) to streamline the text spotting pipeline. Our contributions are threefold: (i) Pipeline simplification: For the first time, we eliminate the text region retrieval step, allowing characters to be directly spotted from scene images. (ii) ROA layer: We introduce a novel RoI (Region of Interest) feature sampling layer for multi-oriented character region feature sampling, significantly enhancing the recognizer’s performance. (iii) Progressive learning strategy: We propose a progressive learning strategy to gradually bridge the gap between synthetic data and real-world images, addressing the challenge posed by the high cost of character-level annotations required during training. Extensive experiments demonstrate that our proposed method is robust and effective across horizontal, oriented, and curved text, achieving results comparable to state-of-the-art methods on ICDAR 2013, ICDAR 2015, Total-Text and ReCTS.