  Recently, transformer-based methods have achieved promising progresses in
object detection, as they can eliminate the post-processes like NMS and enrich
the deep representations. However, these methods cannot well cope with scene
text due to its extreme variance of scales and aspect ratios. In this paper, we
present a simple yet effective transformer-based architecture for scene text
detection. Different from previous approaches that learn robust deep
representations of scene text in a holistic manner, our method performs scene
text detection based on a few representative features, which avoids the
disturbance by background and reduces the computational cost. Specifically, we
first select a few representative features at all scales that are highly
relevant to foreground text. Then, we adopt a transformer for modeling the
relationship of the sampled features, which effectively divides them into
reasonable groups. As each feature group corresponds to a text instance, its
bounding box can be easily obtained without any post-processing operation.
Using the basic feature pyramid network for feature extraction, our method
consistently achieves state-of-the-art results on several popular datasets for
scene text detection.
