Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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}
+}
+```