| --- |
| 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} |
| } |
| ``` |
| |
