Papers › DiffusionSTR: Diffusion Model for Scene Text Recognition
DiffusionSTR: Diffusion Model for Scene Text Recognition
Masato Fujitake
This paper presents Diffusion Model for Scene Text Recognition (DiffusionSTR), an end-to-end text recognition framework using diffusion models for recognizing text in the wild. While existing studies have viewed the scene text recognition task as an image-to-text transformation, we rethought it as a text-text one under images in a diffusion model. We show for the first time that the diffusion model can be applied to text recognition. Furthermore, experimental results on publicly available datasets show that the proposed method achieves competitive accuracy compared to state-of-the-art methods.
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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 Recognition | CUTE80 | DiffusionSTR | Accuracy | 92.5 | #15 of 18 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2013 | DiffusionSTR | Accuracy | 97.1 | #18 of 38 | Archive leaderboard | report |
| Scene Text Recognition | ICDAR2015 | DiffusionSTR | Accuracy | 86 | #13 of 27 | Archive leaderboard | report |
| Scene Text Recognition | IIIT5k | DiffusionSTR | Accuracy | 97.3 | #12 of 17 | Archive leaderboard | report |
| Scene Text Recognition | SVT | DiffusionSTR | Accuracy | 93.6 | #20 of 37 | Archive leaderboard | report |
| Scene Text Recognition | SVTP | DiffusionSTR | Accuracy | 89.2 | #16 of 17 | 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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