  Video text spotting(VTS) is the task that requires simultaneously detecting,
tracking and recognizing text in the video. Existing video text spotting
methods typically develop sophisticated pipelines and multiple models, which is
not friend for real-time applications. Here we propose a real-time end-to-end
video text spotter with Contrastive Representation learning (CoText). Our
contributions are three-fold: 1) CoText simultaneously address the three tasks
(e.g., text detection, tracking, recognition) in a real-time end-to-end
trainable framework. 2) With contrastive learning, CoText models long-range
dependencies and learning temporal information across multiple frames. 3) A
simple, lightweight architecture is designed for effective and accurate
performance, including GPU-parallel detection post-processing, CTC-based
recognition head with Masked RoI. Extensive experiments show the superiority of
our method. Especially, CoText achieves an video text spotting IDF1 of 72.0% at
41.0 FPS on ICDAR2015video, with 10.5% and 32.0 FPS improvement the previous
best method. The code can be found at github.com/weijiawu/CoText.
