Papers › SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression
SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression
Qingwen Bu, Sungrae Park, Minsoo Khang, Yichuan Cheng
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Detection | IC19-Art | SRFormer (ResNet-50) | H-Mean | 79.3 | #2 of 4 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | SRFormer (ResNet-50) | F-Measure | 89.6 | #2 of 17 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | SRFormer (ResNet-50) | Precision | 91.6 | #2 of 17 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | SRFormer (ResNet-50) | Recall | 87.7 | #2 of 17 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | SRFormer (ResNet-50) | F-Measure | 90.0% | #2 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | SRFormer (ResNet-50) | Precision | 92.2% | #2 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | SRFormer (ResNet-50) | Recall | 87.9% | #2 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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