---
license: mit
pretty_name: TextEraseBench
task_categories:
- image-to-image
tags:
- text-removal
- object-removal
- image-inpainting
- mask-conditioned-editing
- osor
---

# TextEraseBench

TextEraseBench is a text-removal benchmark for evaluating object removal and inpainting methods on text overlays and scene text-like objects.

Dataset page: https://huggingface.co/datasets/QinmingZhou/TextEraseBench

## Dataset Summary

TextEraseBench contains 185 paired samples. Each sample includes an input image containing target text, a paired clean background, and a removal mask. The benchmark is designed to test whether methods can remove text while preserving the surrounding background structure.

## Dataset Structure

```text
TextEraseBench/
├── shot/   # text-present input images
├── bg/     # paired clean background images
└── mask/   # target text/effect masks
```

For each sample id `<stem>`:

```text
shot/<stem>.png
bg/<stem>.png
mask/<stem>.png
```

## Usage

Use `shot` as the input image, `mask` as the removal condition, and `bg` as the paired target for evaluation.

## Dataset Creation

TextEraseBench is constructed through a manual-to-automated pipeline. Target text regions are manually annotated with fine-grained bounding boxes, then removed to create paired clean backgrounds. Samples undergo secondary verification to filter artifacts and semantic inconsistencies.

## Intended Use

TextEraseBench is intended for evaluating text removal, object removal, and image inpainting models on paired-background examples.