Datasets:
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
TextEraseBench/
├── shot/ # text-present input images
├── bg/ # paired clean background images
└── mask/ # target text/effect masks
For each sample id <stem>:
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.