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Current video text spotting methods can achieve preferable performance,powered with sufficient labeled training data. However, labeling data manuallyis time-consuming and labor-intensive. To overcome this, using low-costsynthetic data is a promising alternative. This paper introduces a novel videotext synthesis technique called FlowText, which utilizes optical flowestimation to synthesize a large amount of text video data at a low cost fortraining robust video text spotters. Unlike existing methods that focus onimage-level synthesis, FlowText concentrates on synthesizing temporalinformation of text instances across consecutive frames using optical flow.This temporal information is crucial for accurately tracking and spotting textin video sequences, including text movement, distortion, appearance,disappearance, shelter, and blur. Experiments show that combining generaldetectors like TransDETR with the proposed FlowText produces remarkable resultson various datasets, such as ICDAR2015video and ICDAR2013video. Code isavailable at https://github.com/callsys/FlowText.

FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation