Papers › SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and...
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
Mingxin Huang, Yuliang Liu, Zhenghao Peng, Chongyu Liu, Dahua Lin, Shenggao Zhu, Nicholas Yuan, Kai Ding, Lianwen Jin
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly take advantage of the feature interaction between the two tasks. In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer encoder with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism to explicitly guide text localization through recognition loss. The straightforward design results in a concise framework that requires neither additional rectification module nor character-level annotation for the arbitrarily-shaped text. Qualitative and quantitative experiments on multi-oriented datasets RoIC13 and ICDAR 2015, arbitrarily-shaped datasets Total-Text and CTW1500, and multi-lingual datasets ReCTS (Chinese) and VinText (Vietnamese) demonstrate SwinTextSpotter significantly outperforms existing methods. Code is available at https://github.com/mxin262/SwinTextSpotter.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Spotting | ICDAR 2015 | SwinTextSpotter | F-measure (%) - Generic Lexicon | 70.5 | #9 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | SwinTextSpotter | F-measure (%) - Strong Lexicon | 83.9 | #9 of 18 | Archive leaderboard | report |
| Text Spotting | ICDAR 2015 | SwinTextSpotter | F-measure (%) - Weak Lexicon | 77.3 | #9 of 18 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | SwinTextSpotter | F-measure (%) - Full Lexicon | 67.9 | #3 of 9 | Archive leaderboard | report |
| Text Spotting | Inverse-Text | SwinTextSpotter | F-measure (%) - No Lexicon | 55.4 | #3 of 9 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | SwinTextSpotter | F-Measure (%) - Full Lexicon | 77.0 | #10 of 11 | Archive leaderboard | report |
| Text Spotting | SCUT-CTW1500 | SwinTextSpotter | F-measure (%) - No Lexicon | 51.8 | #10 of 11 | Archive leaderboard | report |
| Text Spotting | Total-Text | SwinTextSpotter | F-measure (%) - Full Lexicon | 84.1 | #8 of 12 | Archive leaderboard | report |
| Text Spotting | Total-Text | SwinTextSpotter | F-measure (%) - No Lexicon | 74.3 | #8 of 12 | 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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