  Video Text Spotting (VTS) is a fundamental visual task that aims to predict
the trajectories and content of texts in a video. Previous works usually
conduct local associations and apply IoU-based distance and complex
post-processing procedures to boost performance, ignoring the abundant temporal
information and the morphological characteristics in VTS. In this paper, we
propose a novel Global Video Text Spotting Transformer GloTSFormer to model the
tracking problem as global associations and utilize the Gaussian Wasserstein
distance to guide the morphological correlation between frames. Our main
contributions can be summarized as three folds. 1). We propose a
Transformer-based global tracking method GloTSFormer for VTS and associate
multiple frames simultaneously. 2). We introduce a Wasserstein distance-based
method to conduct positional associations between frames. 3). We conduct
extensive experiments on public datasets. On the ICDAR2015 video dataset,
GloTSFormer achieves 56.0 MOTA with 4.6 absolute improvement compared with the
previous SOTA method and outperforms the previous Transformer-based method by a
significant 8.3 MOTA.
