Video text spotting is still an important research topic due to its various
real-applications. Previous approaches usually fall into the four-staged
pipeline: text detection in individual images, framewisely recognizing
localized text regions, tracking text streams and generating final results with
complicated post-processing skills, which might suffer from the huge
computational cost as well as the interferences of low-quality text. In this
paper, we propose a fast and robust video text spotting framework by only
recognizing the localized text one-time instead of frame-wisely recognition.
Specifically, we first obtain text regions in videos with a well-designed
spatial-temporal detector. Then we concentrate on developing a novel text
recommender for selecting the highest-quality text from text streams and only
recognizing the selected ones. Here, the recommender assembles text tracking,
quality scoring and recognition into an end-to-end trainable module, which not
only avoids the interferences from low-quality text but also dramatically
speeds up the video text spotting process. In addition, we collect a larger
scale video text dataset (LSVTD) for promoting the video text spotting
community, which contains 100 text videos from 22 different real-life
scenarios. Extensive experiments on two public benchmarks show that our method
greatly speeds up the recognition process averagely by 71 times compared with
the frame-wise manner, and also achieves the remarkable state-of-the-art.

本文提出了一种快速且强健的视频文本识别框架，通过一次性识别局部文本，避免了针对每一帧的文本识别，使用文本推荐器从文本流中选择高质量文本并仅对其进行识别，大大加快了视频文本识别的速度，在公共基准测试中实现了显著的最先进效果，并推出了一个促进视频文本识别社区发展的数据集（LSVTD）.