---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: images
    list: image
  - name: conversations
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: data_source
    dtype: string
  - name: class
    dtype: string
  - name: ori_bbox
    list: string
  splits:
  - name: test
    num_bytes: 986226411
    num_examples: 1061
  - name: train
    num_bytes: 984872941236
    num_examples: 1482028
  download_size: 985226675892
  dataset_size: 985859167647
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
  - split: train
    path: data/train-*
---

# TextPecker-1.5M: A Dataset for Training and evaluating TextPecker

This repository contains the **TextPecker-1.5M** dataset, a new benchmark proposed in the paper "[TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering](https://arxiv.org/abs/2602.xxxxx)".


## Code and Project Page

The official implementation and project details for the TextPecker and TextPecker-1.5M dataset can be found on the GitHub repository:
[https://github.com/CIawevy/TextPecker](https://github.com/CIawevy/TextPecker)

## Sample Usage

You can easily load the TextPecker-1.5M dataset using the Hugging Face `datasets` library. The dataset is provided in two configurations: `train` and `test`

```python
from datasets import load_dataset

# Load the full TextPecker-1.5M dataset (includes train and test splits)
dataset = load_dataset("CIawevy/TextPecker-1.5M", "default")
train_data = dataset["train"]
test_data = dataset["test"]

# Load specific split directly (more efficient for practical usage)
train_data = load_dataset("CIawevy/TextPecker-1.5M", "default", split="train")
test_data = load_dataset("CIawevy/TextPecker-1.5M", "default", split="test")
```

For detailed instructions on installation, model download, evaluation, and running demos with the FreeFine framework, please refer to the [GitHub repository](https://github.com/CIawevy/TextPecker).

## Citation

If you find this dataset useful for your research, please cite the accompanying paper:

```bibtex
@article{zhu2026TextPecker,
  title   = {TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering},
  author  = {Zhu, Hanshen and Liu, Yuliang and Wu, Xuecheng and Wang, An-Lan and Feng, Hao and Yang, Dingkang and Feng, Chao and Huang, Can and Tang, Jingqun and Bai, Xiang},
  journal = {arXiv preprint arXiv:xxxxx},
  year    = {2026}
}
```