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Datasets:
Yesianrohn
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UnionST

Tasks:
Image-to-Text
Languages:
English
Size:
10M<n<100M
Tags:
OCR
STR
scene text
synthetic data
License:
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metadata 
license: mit
task_categories:
  - image-to-text
language:
  - en
tags:
  - OCR
  - STR
  - scene text
  - synthetic data
size_categories:
  - 10M<n<100M

UnionST: A Strong Synthetic Engine for Scene Text Recognition

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".

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 suffer from insufficient diversity (corpus/font/layout) and a large domain gap with real-world text.

Key Advantages

  • 🎯 100% Label Correctness: Rendering-based paradigm ensures accurate labels (unlike generative models with aesthetic but error-prone outputs).
  • ⚡ Cost-Efficiency: CPU-based generation costs only 1/20 of diffusion-based methods and 1/10,000 of closed-source alternatives.
  • 🚀 Strong Performance: UnionST-S (5M samples) outperforms 36M-scale traditional synthetic datasets on challenging STR benchmarks.

Dataset

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.

Training Model

OpenOCR

cd OpenOCR
torchrun  --nproc_per_node=8 tools/train_rec.py --c configs/rec/nrtr/svtrv2_nrtr_unionst.yml

Some of our trained models can be found at Huggingface.

Citation 

TBD

License 

"""
UnionST
Copyright (c) 2025-present YesianRohn
Based on SynthTIGER
Copyright (c) 2021-present NAVER Corp.
MIT License
"""

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

Acknowledgements