Yesianrohn/STR-Synth

---
language:
- en
license: apache-2.0
size_categories:
- 10M<n<100M
task_categories:
- image-to-text
tags:
- ocr
- Scene Text Recognition
- synthetic data
- lmdb
- computer vision
---

# STR-Synth

[**Paper**](https://huggingface.co/papers/2602.06450) | [**GitHub**](https://github.com/YesianRohn/UnionST)

This repository serves as the supplementary dataset resource for the paper *What’s Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution*, dedicated to summarizing and providing the representative synthetic datasets for Scene Text Recognition (STR) used in the paper's comparative experiments.

The dataset collection in this repository aggregates **6 mainstream STR synthetic datasets**: MJ, ST, SynthAdd, CurvedST, SynthTIGER, and UnrealText, with a total of **46 million samples** in total, covering a diverse range of scene text simulation scenarios and characteristics to support comprehensive comparative research on STR synthetic data performance.

In line with industry standards, all datasets in this repository are provided in the **lmdb file format**—the de facto standard adopted by the mainstream STR research protocol, ensuring seamless compatibility with most existing STR training and evaluation frameworks for straightforward integration and usage.

For the self-developed high-performance synthetic STR dataset proposed in the paper, **UnionST** (featuring superior diversity, label accuracy and cost-efficiency), please refer to its dedicated repository at: https://huggingface.co/datasets/Yesianrohn/UnionST.

## Citation

```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}
}
```

---
语言：
- 英语
许可证：Apache 2.0
规模类别：
- 1000万 < 样本数 < 1亿
任务类别：
- 图像到文本
标签：
- 光学字符识别（Optical Character Recognition, OCR
- 场景文本识别（Scene Text Recognition, STR
- 合成数据
- lmdb
- 计算机视觉
---

# STR-Synth

[**论文**](https://huggingface.co/papers/2602.06450) | [**GitHub**](https://github.com/YesianRohn/UnionST)

本仓库为论文《What’s Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution》的配套数据集资源，旨在汇总并提供该论文对比实验中所使用的各类代表性场景文本识别（Scene Text Recognition, STR）合成数据集。

本仓库收录的数据集集合包含**6款主流STR合成数据集：MJ、ST、SynthAdd、CurvedST、SynthTIGER以及UnrealText，总样本量达**4600万**，覆盖多样化的场景文本仿真场景与特征，可支撑STR合成数据性能的全面对比研究。

遵循行业规范，本仓库内所有数据集均以**lmdb文件格式**提供——这是当前主流STR研究协议所采用的事实标准，可与绝大多数现有STR训练与评估框架无缝兼容，便于直接集成与使用。

对于论文中提出的自研高性能STR合成数据集**UnionST**（具备优异的多样性、标签准确率与成本效益），请访问其专属仓库：https://huggingface.co/datasets/Yesianrohn/UnionST。

## 引用

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}