  Recently, video scene text detection has received increasing attention due to
its comprehensive applications. However, the lack of annotated scene text video
datasets has become one of the most important problems, which hinders the
development of video scene text detection. The existing scene text video
datasets are not large-scale due to the expensive cost caused by manual
labeling. In addition, the text instances in these datasets are too clear to be
a challenge. To address the above issues, we propose a tracking based
semi-automatic labeling strategy for scene text videos in this paper. We get
semi-automatic scene text annotation by labeling manually for the first frame
and tracking automatically for the subsequent frames, which avoid the huge cost
of manual labeling. Moreover, a paired low-quality scene text video dataset
named Text-RBL is proposed, consisting of raw videos, blurry videos, and
low-resolution videos, labeled by the proposed convenient semi-automatic
labeling strategy. Through an averaging operation and bicubic down-sampling
operation over the raw videos, we can efficiently obtain blurry videos and
low-resolution videos paired with raw videos separately. To verify the
effectiveness of Text-RBL, we propose a baseline model combined with the text
detector and tracker for video scene text detection. Moreover, a failure
detection scheme is designed to alleviate the baseline model drift issue caused
by complex scenes. Extensive experiments demonstrate that Text-RBL with paired
low-quality videos labeled by the semi-automatic method can significantly
improve the performance of the text detector in low-quality scenes.
