  Existing OCR engines or document image analysis systems typically rely on
training separate models for text detection in varying scenarios and
granularities, leading to significant computational complexity and resource
demands. In this paper, we introduce "Detect Any Text" (DAT), an advanced
paradigm that seamlessly unifies scene text detection, layout analysis, and
document page detection into a cohesive, end-to-end model. This design enables
DAT to efficiently manage text instances at different granularities, including
*word*, *line*, *paragraph* and *page*. A pivotal innovation in DAT is the
across-granularity interactive attention module, which significantly enhances
the representation learning of text instances at varying granularities by
correlating structural information across different text queries. As a result,
it enables the model to achieve mutually beneficial detection performances
across multiple text granularities. Additionally, a prompt-based segmentation
module refines detection outcomes for texts of arbitrary curvature and complex
layouts, thereby improving DAT's accuracy and expanding its real-world
applicability. Experimental results demonstrate that DAT achieves
state-of-the-art performances across a variety of text-related benchmarks,
including multi-oriented/arbitrarily-shaped scene text detection, document
layout analysis and page detection tasks.
