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 this https URL

本文介绍了一种名为 FlowText 的视频文字合成技术，通过利用光流估计，在低成本的情况下合成大量的文本视频数据，用于训练强健的视频文字定位器。实验证明，将 TransDETR 等通用检测器与 FlowText 结合使用，可以在 ICDAR2015video 和 ICDAR2013video 等不同数据集上获得显著的结果。

使用光流估计合成逼真的场景文本视频

FlowText: Synthesizing Realistic Scene Text Video with Optical Flow  Estimation

Recent video text spotting methods usually require the three-staged pipeline,
i.e., detecting text in individual images, recognizing localized text, tracking
text streams with post-processing to generate final results. These methods
typically follow the tracking-by-match paradigm and develop sophisticated
pipelines. In this paper, rooted in Transformer sequence modeling, we propose a
simple, but effective end-to-end video text DEtection, Tracking, and
Recognition framework (TransDETR). TransDETR mainly includes two advantages: 1)
Different from the explicit match paradigm in the adjacent frame, TransDETR
tracks and recognizes each text implicitly by the different query termed text
query over long-range temporal sequence (more than 7 frames). 2) TransDETR is
the first end-to-end trainable video text spotting framework, which
simultaneously addresses the three sub-tasks (e.g., text detection, tracking,
recognition). Extensive experiments in four video text datasets (i.e.,ICDAR2013
Video, ICDAR2015 Video, Minetto, and YouTube Video Text) are conducted to
demonstrate that TransDETR achieves state-of-the-art performance with up to
around 8.0% improvements on video text spotting tasks. The code of TransDETR
can be found at this https URL.

本研究基于 Transformer 序列建模，提出了一个简单而有效的端到端视频文本识别框架 TransDETR，它能够同时处理文本检测、跟踪和识别，通过两个主要优点：1) 在长序列中使用文本查询跟踪和识别每个文本，2) 在四个视频文本数据集上验证 TransDETR 具有最先进的性能，其中视频文本识别任务性能提高了 8.0％左右。