Add model card for UnionST

README.md

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+---
+license: mit
+pipeline_tag: image-to-text
+tags:
+- ocr
+- scene-text-recognition
+- synthetic-data
+---
+
+# UnionST: A Strong Synthetic Engine for Scene Text Recognition
+
+This repository contains model checkpoints for **UnionST**, introduced in the paper [What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution](https://huggingface.co/papers/2602.06450).
+
+## Introduction
+Scene Text Recognition (STR) relies critically on large-scale, high-quality training data. While synthetic data provides a cost-effective alternative to manually annotated real data, existing rendering-based synthetic datasets often suffer from a domain gap with real-world text due to insufficient diversity. 
+
+UnionST is a strong data engine that synthesizes text covering a union of challenging samples to better align with the complexity observed in the wild. Models trained on the resulting UnionST-S dataset achieve significant improvements over traditional synthetic datasets on challenging STR benchmarks.
+
+## Resources
+- **Paper:** [arXiv:2602.06450](https://arxiv.org/abs/2602.06450)
+- **Code:** [GitHub - YesianRohn/UnionST](https://github.com/YesianRohn/UnionST)
+- **Datasets:** [UnionST Dataset on Hugging Face](https://huggingface.co/datasets/Yesianrohn/UnionST)
+
+## Training
+The models (such as SVTRv2-AR) are implemented using the [OpenOCR](https://github.com/Topdu/OpenOCR) framework. Training can be initiated with:
+
+```bash
+cd OpenOCR
+torchrun --nproc_per_node=8 tools/train_rec.py --c configs/rec/nrtr/svtrv2_nrtr_unionst.yml
+```
+
+## Citation
+If you find this work useful, please cite:
+```bibtex
+@inproceedings{ye2026wrong,
+  title={What's Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution},
+  author={Ye, Xingsong and Du, Yongkun and Zhang, JiaXin and Li, Chen and LYU, Jing and Chen, Zhineng},
+  booktitle={CVPR},
+  year={2026}
+}
+```
+
+## License
+This project is licensed under the MIT License.