  Sequence generation models have recently made significant progress in
unifying various vision tasks. Although some auto-regressive models have
demonstrated promising results in end-to-end text spotting, they use specific
detection formats while ignoring various text shapes and are limited in the
maximum number of text instances that can be detected. To overcome these
limitations, we propose a UNIfied scene Text Spotter, called UNITS. Our model
unifies various detection formats, including quadrilaterals and polygons,
allowing it to detect text in arbitrary shapes. Additionally, we apply
starting-point prompting to enable the model to extract texts from an arbitrary
starting point, thereby extracting more texts beyond the number of instances it
was trained on. Experimental results demonstrate that our method achieves
competitive performance compared to state-of-the-art methods. Further analysis
shows that UNITS can extract a larger number of texts than it was trained on.
We provide the code for our method at https://github.com/clovaai/units.
