---
license: apache-2.0
task_categories:
  - image-to-text
language:
  - en
tags:
  - scene-text-recognition
  - STR
  - OCR
  - artistic-text
  - wordart
  - synthetic-data
  - lmdb
size_categories:
  - 1M<n<10M
pretty_name: WATER-Data
configs: []
---

# WATER-Data: Datasets for WordArt-Oriented Scene Text Recognition

**WATER-Data** is the official dataset release for the paper
**"Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods" (ECCV 2026)**.

WordArt (artistic text) features highly customized fonts, textures, and layouts, making
**WA**ordArt-oriented scene **TE**xt **R**ecognition (**WATER**) substantially more challenging
than general Scene Text Recognition (STR). The primary bottleneck for WATER is the lack of
large-scale, stylistically diverse, and reliably annotated data. WATER-Data addresses this gap
by providing a large-scale synthetic suite, a carefully deduplicated real training set, and a
dedicated artistic-text benchmark.

- 📄 **Paper (arXiv):** https://arxiv.org/abs/2606.24484
- 💻 **Code:** https://github.com/YesianRohn/WATER
- 🧠 **Model code (OpenOCR-WATERec):** https://github.com/YesianRohn/OpenOCR-WATERec
- 🏋️ **Model weights:** https://huggingface.co/Yesianrohn/WATERec-Models
- 🖋️ **Artistic fonts:** https://huggingface.co/datasets/Yesianrohn/artistic-fonts
- 📝 **WATER-Z captions:** https://huggingface.co/datasets/Yesianrohn/WATER-Z_Captions

---

## Dataset Overview

WATER-Data contains three components: a synthetic training suite (**WATER-S**), a real training
set (**WATER-R**), and an artistic-text evaluation benchmark (**WordArt-Bench**).

| Component | Subset | Role | #Instances | Source |
|-----------|--------|------|-----------|--------|
| **WATER-S** | WATER-T | Synthetic train | ~1M | Tool-based rendering (SynthWordArt) |
| **WATER-S** | WATER-Z | Synthetic train | ~1M | Generative model (Qwen3-VL + Z-Image) |
| **WATER-R** | – | Real train | 3,225,130 | Union14M-L + WordArt-Train + WAS-R (deduplicated) |
| **WordArt-Bench** | – | Evaluation | 1,511 | WordArt test split |

All subsets are English WordArt in the current release.

---

## Directory Structure

Every split is stored as a standalone **LMDB** database (`data.mdb` + `lock.mdb`), the format
used by the [OpenOCR](https://github.com/Topdu/OpenOCR) framework.

```
WATER-Data/
├── WATER-R/                 # Real training set (~11.8 GB)
│   ├── data.mdb
│   └── lock.mdb
├── WATER-S/                 # Synthetic training suite
│   ├── WATER-T/             # Tool-rendered subset
│   │   ├── data.mdb
│   │   └── lock.mdb
│   └── WATER-Z/             # Model-generated subset
│       ├── data.mdb
│       └── lock.mdb
└── WordArt-Bench/           # Artistic-text benchmark (~325 MB)
    ├── data.mdb
    └── lock.mdb
```

---

## Subset Details

### WATER-S — Synthetic Suite (≈2M)
A 2M-scale synthetic artistic-text dataset, improving the scale of existing artistic text data
by hundreds of times. It consists of two complementary subsets:

- **WATER-T (Tool-based Rendering, ~1M).** Generated with **SynthWordArt**, an artistic-text
  rendering engine built on SynthText / SynthTIGER. It replaces standard fonts with a library of
  **11,250 artistic fonts** and adds rich layout patterns (curved lines, vertical text,
  multi-orientation layouts, perspective and stretching). It offers **precise control** over text
  content, font, and layout, with perfectly accurate labels.
- **WATER-Z (Model-based Generation, ~1M).** Generated by an automatic few-shot prompt-mining
  pipeline: **Qwen3-VL-8B** mines fine-grained captions (with an editable text placeholder) from
  real artistic text, expands them into **273,488 high-quality prompts**, and **Z-Image-Turbo**
  synthesizes images at 256×256. It offers **higher realism and diversity** in background texture,
  layout composition, and global visual style.

WATER-T and WATER-Z are complementary: WATER-T provides strong controllability and label accuracy,
while WATER-Z provides natural, design-like style diversity. Training on their combination covers
both the "strongly controlled" and "style-diverse" regimes.

### WATER-R — Real Training Set (3.2M)
A real-world training set re-constructed from three sources:
[Union14M-L](https://github.com/Mountchicken/Union14M),
[WordArt-Train](https://github.com/xdxie/WordArt), and
[WAS-R](https://github.com/xdxie/WordArt). **Strict hashing deduplication** is performed against all
evaluation sets to avoid label leakage. It contains **3,225,130** text instances.

### WordArt-Bench — Evaluation Benchmark
The artistic-text evaluation benchmark (test split of WordArt), with **1,511** images, used to
report recognition accuracy. In the paper, our WATERec baseline reaches **90.40%** accuracy on this
benchmark — the first result to exceed 90% — surpassing both general-purpose and OCR-specialized
vision-language models by a large margin.

---

## Usage

Each LMDB database stores image–label pairs in the OpenOCR convention. A minimal reading example:

```python
import lmdb

env = lmdb.open(
    "WATER-Data/WordArt-Bench",  # folder containing data.mdb / lock.mdb
    readonly=True, lock=False, readahead=False, meminit=False,
)

with env.begin(write=False) as txn:
    num_samples = int(txn.get(b"num-samples"))
    # keys follow the OpenOCR layout, e.g.:
    #   image-000000001 -> raw image bytes
    #   label-000000001 -> ground-truth text
    img_buf = txn.get(b"image-000000001")
    label = txn.get(b"label-000000001").decode("utf-8")

print(num_samples, label)
```

For training and evaluation, we recommend using the official framework
[OpenOCR-WATERec](https://github.com/YesianRohn/OpenOCR-WATERec), which consumes these LMDB
databases directly.

To download the dataset:

```bash
# Requires: pip install -U "huggingface_hub[cli]"
hf download Yesianrohn/WATER-Data --repo-type dataset --local-dir ./WATER-Data
```

---

## Intended Use

WATER-Data is intended for **research** on scene text recognition, especially artistic / WordArt
text. Typical uses include: training and benchmarking STR models, studying synthetic-data
strategies (tool-based vs. generative), and evaluating general / OCR-specialized VLMs on
challenging stylized text.

---

## License

Released under the **Apache 2.0** license. The dataset is built upon publicly available STR data
sources (Union14M-L, WordArt, WAS-R) and synthetic content; please also respect the original
licenses of these underlying datasets.

---

## Citation

If you use WATER-Data in your research, please cite our paper:

```bibtex
@inproceedings{water2026eccv,
  title     = {Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods},
  author    = {Ye, Xingsong and Du, Yongkun and Zhang, Jiaxin and Zhang, Haojie and Sun, Chong and Li, Chen and Lyu, Jing and Chen, Zhineng},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```