  The adaptation capability to a wide range of domains is crucial for scene
text spotting models when deployed to real-world conditions. However, existing
state-of-the-art (SOTA) approaches usually incorporate scene text detection and
recognition simply by pretraining on natural scene text datasets, which do not
directly exploit the intermediate feature representations between multiple
domains. Here, we investigate the problem of domain-adaptive scene text
spotting, i.e., training a model on multi-domain source data such that it can
directly adapt to target domains rather than being specialized for a specific
domain or scenario. Further, we investigate a transformer baseline called
Swin-TESTR to focus on solving scene-text spotting for both regular and
arbitrary-shaped scene text along with an exhaustive evaluation. The results
clearly demonstrate the potential of intermediate representations to achieve
significant performance on text spotting benchmarks across multiple domains
(e.g. language, synth-to-real, and documents). both in terms of accuracy and
efficiency.
