  Existing techniques for text detection can be broadly classified into two
primary groups: segmentation-based and regression-based methods. Segmentation
models offer enhanced robustness to font variations but require intricate
post-processing, leading to high computational overhead. Regression-based
methods undertake instance-aware prediction but face limitations in robustness
and data efficiency due to their reliance on high-level representations. In our
academic pursuit, we propose SRFormer, a unified DETR-based model with
amalgamated Segmentation and Regression, aiming at the synergistic harnessing
of the inherent robustness in segmentation representations, along with the
straightforward post-processing of instance-level regression. Our empirical
analysis indicates that favorable segmentation predictions can be obtained at
the initial decoder layers. In light of this, we constrain the incorporation of
segmentation branches to the first few decoder layers and employ progressive
regression refinement in subsequent layers, achieving performance gains while
minimizing computational load from the mask.Furthermore, we propose a
Mask-informed Query Enhancement module. We take the segmentation result as a
natural soft-ROI to pool and extract robust pixel representations, which are
then employed to enhance and diversify instance queries. Extensive
experimentation across multiple benchmarks has yielded compelling findings,
highlighting our method's exceptional robustness, superior training and data
efficiency, as well as its state-of-the-art performance. Our code is available
at https://github.com/retsuh-bqw/SRFormer-Text-Det.
