---
license: cc-by-4.0
pretty_name: TextEraseBench
task_categories:
- image-to-image
tags:
- text-removal
- scene-text
- image-inpainting
- benchmark
---

# TextEraseBench

[Paper](https://arxiv.org/abs/2606.28094) | [Code](https://github.com/Zhouqm-Git/osor)

TextEraseBench is a paired benchmark for scene-text removal. It contains **185** samples drawn from a mixture of synthetic scenes and real-world photographs captured by the authors.

## Construction

1. Fine-grained text regions were manually annotated in each source image.
2. Nano Banana 2 was used to remove the selected text and generate the paired text-free background.
3. The resulting pairs were checked again to remove failed edits and visible artifacts.

## Data Structure

```text
TextEraseBench/
├── shot/    # Source images containing text
├── bg/      # Paired text-free backgrounds
└── mask/    # Fine-grained text masks
```

Files with the same relative name form one evaluation sample.

## Evaluation

Use `shot/` and `mask/` as model inputs and `bg/` as the reconstruction target. Report results over the complete 185-sample set and keep preprocessing settings consistent across compared methods.

## License

TextEraseBench is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) to the extent that the dataset authors hold rights in the images and annotations. Some paired backgrounds were produced using the Gemini/Nano Banana service; users remain responsible for complying with any applicable service terms and for assessing their intended use.

## Citation

```bibtex
@inproceedings{zhou2026osor,
  title     = {OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal},
  author    = {Zhou, Qinming and Sun, Chenxi and Kong, Deyang and He, Junhao and Tang, Xiangheng and Yu, Peike and Wu, Haotian and Cao, Leilei and Zhang, Linfeng},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2606.28094}
}
```