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--- |
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license: mit |
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task_categories: |
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- image-to-text |
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language: |
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- en |
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tags: |
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- OCR |
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- STR |
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- scene text |
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- synthetic data |
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size_categories: |
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- 10M<n<100M |
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--- |
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# UnionST: A Strong Synthetic Engine for Scene Text Recognition |
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Official data synthesis code of the paper *"What’s Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution"*. |
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## Introduction |
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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 suffer from **insufficient diversity** (corpus/font/layout) and a large domain gap with real-world text. |
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### Key Advantages |
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- 🎯 **100% Label Correctness**: Rendering-based paradigm ensures accurate labels (unlike generative models with aesthetic but error-prone outputs). |
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- ⚡ **Cost-Efficiency**: CPU-based generation costs only 1/20 of diffusion-based methods and 1/10,000 of closed-source alternatives. |
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- 🚀 **Strong Performance**: UnionST-S (5M samples) outperforms 36M-scale traditional synthetic datasets on challenging STR benchmarks. |
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## Dataset |
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UnionST-S, UnionST-P, and UnionST-R datasets (each containing 5M samples) can be downloaded here. We use the lmdb file format adopted by the mainstream STR protocol. |
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## Training Model |
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[OpenOCR](https://github.com/Topdu/OpenOCR) |
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```bash |
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cd OpenOCR |
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torchrun --nproc_per_node=8 tools/train_rec.py --c configs/rec/nrtr/svtrv2_nrtr_unionst.yml |
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``` |
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Some of our trained models can be found at [Huggingface](https://huggingface.co/Yesianrohn/UnionST-Models). |
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## Citation |
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```bash |
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TBD |
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``` |
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## License |
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```bash |
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""" |
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UnionST |
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Copyright (c) 2025-present YesianRohn |
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Based on SynthTIGER |
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Copyright (c) 2021-present NAVER Corp. |
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MIT License |
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""" |
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Permission is hereby granted, free of charge, to any person obtaining a copy |
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of this software and associated documentation files (the "Software"), to deal |
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in the Software without restriction, including without limitation the rights |
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
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copies of the Software, and to permit persons to whom the Software is |
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furnished to do so, subject to the following conditions: |
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The above copyright notice and this permission notice shall be included in |
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all copies or substantial portions of the Software. |
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN |
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THE SOFTWARE. |
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``` |
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## Acknowledgements |
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- We thank the [SynthText](https://github.com/ankush-me/SynthText), [SynthTIGER](https://github.com/clovaai/synthtiger), [SVTRv2](https://github.com/Topdu/OpenOCR/blob/main/docs/svtrv2.md) and [Union14M](https://github.com/Mountchicken/Union14M) for their open-source code/datasets. |
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- Special thanks also go to the training framework: [OpenOCR](https://github.com/Topdu/OpenOCR). |
