FlowText: Synthesizing Realistic Scene Text Video with Optical Flow
  Estimation
Yuzhong Zhao Weijia Wu Zhuang Li Jiahong Li Weiqiang Wang
Current video text spotting methods can achieve preferable performance,
powered with sufficient labeled training data. However, labeling data manually
is time-consuming and labor-intensive. To overcome this, using low-cost
synthetic data is a promising alternative. This paper introduces a novel video
text synthesis technique called FlowText, which utilizes optical flow
estimation to synthesize a large amount of text video data at a low cost for
training robust video text spotters. Unlike existing methods that focus on
image-level synthesis, FlowText concentrates on synthesizing temporal
information of text instances across consecutive frames using optical flow.
This temporal information is crucial for accurately tracking and spotting text
in video sequences, including text movement, distortion, appearance,
disappearance, shelter, and blur. Experiments show that combining general
detectors like TransDETR with the proposed FlowText produces remarkable results
on various datasets, such as ICDAR2015video and ICDAR2013video. Code is
available at https://github.com/callsys/FlowText.