Papers › DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Bo Du, DaCheng Tao
Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling.In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://github.com/ymy-k/DPText-DETR.
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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 | DPText-DETR (ResNet-50) | H-Mean | 78.1 | #4 of 4 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | DPText-DETR (ResNet50) | F-Measure | 88.8 | #3 of 17 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | DPText-DETR (ResNet50) | Precision | 91.7 | #3 of 17 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | DPText-DETR (ResNet50) | Recall | 86.2 | #3 of 17 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | DPText-DETR (ResNet-50) | F-Measure | 89.0% | #3 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | DPText-DETR (ResNet-50) | FPS | 17 | #3 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | DPText-DETR (ResNet-50) | Precision | 91.8% | #3 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | DPText-DETR (ResNet-50) | Recall | 86.4% | #3 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.
Methods
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