Pointer Networks for Arbitrary-Shaped Text Spotting

Current text spotting methods perform text detection and text recognition separately. However, in complex scenes where bounding boxes of texts with various shapes are often overlapped, text detection becomes error-prone. By contrast, character detection is more non-ambiguous and easier to learn. In this paper, we present a highly efficient one-stage method named PointerNet for arbitrary-shaped text spotting. Unlike previous methods, PointerNet does not rely on text detection and opens a novel spotting-by-character-detection paradigm. In particular, to connect characters to texts, we propose a simple yet highly effective strategy named pointer that learns the 2D offset from the center of the current character to the center of the subsequent character. Evaluations demonstrate that our PointerNet achieves state-of-the-art performance and is more efficient than current methods (75ms vs. 133ms compared with FOTS). Our code will be publicly available.