Papers › TextCohesion: Detecting Text for Arbitrary Shapes
TextCohesion: Detecting Text for Arbitrary Shapes
Weijia Wu, Jici Xing, Hong Zhou
In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions. These components are easier to handle than the entire text instance. A confidence scoring mechanism is designed to filter characters that are similar to text. Our method can integrate text contexts intensively when backgrounds are complex. Experiments on two curved challenging benchmarks demonstrate that TextCohesion outperforms state-of-the-art methods, achieving the F-measure of 84.6% on Total-Text and bfseries86.3% on SCUT-CTW1500.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Curved Text Detection | SCUT-CTW1500 | TextCohesion | F-Measure | 86.3% | #1 of 5 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | TextCohesion | F-Measure | 84.6% | #16 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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