  Most existing video text spotting benchmarks focus on evaluating a single
language and scenario with limited data. In this work, we introduce a
large-scale, Bilingual, Open World Video text benchmark dataset(BOVText). There
are four features for BOVText. Firstly, we provide 2,000+ videos with more than
1,750,000+ frames, 25 times larger than the existing largest dataset with
incidental text in videos. Secondly, our dataset covers 30+ open categories
with a wide selection of various scenarios, e.g., Life Vlog, Driving, Movie,
etc. Thirdly, abundant text types annotation (i.e., title, caption or scene
text) are provided for the different representational meanings in video.
Fourthly, the BOVText provides bilingual text annotation to promote multiple
cultures live and communication. Besides, we propose an end-to-end video text
spotting framework with Transformer, termed TransVTSpotter, which solves the
multi-orient text spotting in video with a simple, but efficient
attention-based query-key mechanism. It applies object features from the
previous frame as a tracking query for the current frame and introduces a
rotation angle prediction to fit the multiorient text instance. On
ICDAR2015(video), TransVTSpotter achieves the state-of-the-art performance with
44.1% MOTA, 9 fps. The dataset and code of TransVTSpotter can be found at
github:com=weijiawu=BOVText and github:com=weijiawu=TransVTSpotter,
respectively.
