Update README.md

README.md

@@ -34,3 +34,45 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# TextPecker-1.5M: A Dataset for Training and evaluating TextPecker
+
+This repository contains the **TextPecker** 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/FreeFine).
+
+## 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}
+}
+```