  Video text spotting (VTS) aims to simultaneously localize, recognize and
track text instances in videos. To address the limited recognition capability
of end-to-end methods, recent methods track the zero-shot results of
state-of-the-art image text spotters directly, and achieve impressive
performance. However, owing to the domain gap between different datasets, these
methods usually obtain limited tracking trajectories on extreme dataset.
Fine-tuning transformer-based text spotters on specific datasets could yield
performance enhancements, albeit at the expense of considerable training
resources. In this paper, we propose a Language Collaboration and Glyph
Perception Model, termed LOGO, an innovative framework designed to enhance the
performance of conventional text spotters. To achieve this goal, we design a
language synergy classifier (LSC) to explicitly discern text instances from
background noise in the recognition stage. Specially, the language synergy
classifier can output text content or background code based on the legibility
of text regions, thus computing language scores. Subsequently, fusion scores
are computed by taking the average of detection scores and language scores, and
are utilized to re-score the detection results before tracking. By the
re-scoring mechanism, the proposed LSC facilitates the detection of
low-resolution text instances while filtering out text-like regions. Moreover,
the glyph supervision is introduced to enhance the recognition accuracy of
noisy text regions. In addition, we propose the visual position mixture module,
which can merge the position information and visual features efficiently, and
acquire more discriminative tracking features. Extensive experiments on public
benchmarks validate the effectiveness of the proposed method.
