GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
Video text spotting (VTS) extends image text spotting (ITS) by adding text
tracking, significantly increasing task complexity. Despite progress in VTS,
existing methods still fall short of the performance seen in ITS. This paper
identifies a key limitation in current video text spotters: limited recognition
capability, even after extensive end-to-end training. To address this, we
propose GoMatching++, a parameter- and data-efficient method that transforms an
off-the-shelf image text spotter into a video specialist. The core idea lies in
freezing the image text spotter and introducing a lightweight, trainable
tracker, which can be optimized efficiently with minimal training data. Our
approach includes two key components: (1) a rescoring mechanism to bridge the
domain gap between image and video data, and (2) the LST-Matcher, which
enhances the frozen image text spotter's ability to handle video text. We
explore various architectures for LST-Matcher to ensure efficiency in both
parameters and training data. As a result, GoMatching++ sets new performance
records on challenging benchmarks such as ICDAR15-video, DSText, and BOVText,
while significantly reducing training costs. To address the lack of curved text
datasets in VTS, we introduce ArTVideo, a new benchmark featuring over 30%
curved text with detailed annotations. We also provide a comprehensive
statistical analysis and experimental results for ArTVideo. We believe that
GoMatching++ and the ArTVideo benchmark will drive future advancements in video
text spotting. The source code, models and dataset are publicly available at
https://github.com/Hxyz-123/GoMatching.
