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.

翻译：序列生成模型近期在统一多种视觉任务方面取得了显著进展。尽管部分自回归模型在端到端文本识别中展现出良好效果，但它们采用特定检测格式而忽略了多样的文本形状，且能检测的文本实例数量存在上限。为克服这些局限，我们提出统一场景文本识别器UNITS。该模型统一了四边形与多边形等多种检测格式，从而能够检测任意形状的文本。此外，我们通过起始点提示机制使模型能从任意起点提取文本，从而提取超过训练实例数量的更多文本。实验结果表明，与现有最优方法相比，我们的方法取得了具有竞争力的性能。进一步分析显示，UNITS能提取超出其训练数量的文本。我们已在 https://github.com/clovaai/units 公开该方法代码。