When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance.

现实世界嘈杂环境下，自主场景文本检测系统通用于多个领域的能力至关重要，本研究探索和研究了领域无关的场景文本检测问题，提出了一个名为Under-Water Text (UWT)的检测基准，同时设计了一个高效的基于超分辨率的端到端转换器，实现了在常规和任意形状场景中比现有文本检测架构更准确和高效的性能。

深入多领域嘈杂场景中的文本识别