WATERec Models

WATERec-Models: Strong Baseline for WordArt-Oriented Scene Text Recognition

WATERec is the strong STR baseline proposed in the paper "Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods" (ECCV 2026). It couples a NaViT-like RoPE-ViT encoder that supports arbitrary-shaped inputs with an autoregressive (AR) Transformer decoder, structurally breaking the bottleneck of fixed-template STR on highly irregular WordArt.

This repository hosts the trained model checkpoints.

• 📄 Paper (arXiv): https://arxiv.org/abs/2606.24484
• 💻 Code: https://github.com/YesianRohn/WATER
• 🧠 Model code (OpenOCR-WATERec): https://github.com/YesianRohn/OpenOCR-WATERec
• 📦 Datasets (WATER-Data): https://huggingface.co/datasets/Yesianrohn/WATER-Data

---

Model Architecture

• Encoder: 6-layer Transformer with RoPE attention, accepting arbitrary aspect ratios. Inputs are rescaled (aspect-ratio preserving) so the number of 4×4 patch tokens lies in [64, 256]; tokens are projected to d=384 and arranged in row-major order.
• Decoder: 2 cross-attention AR Transformer layers, predicting characters one by one under cross-entropy loss. Max text length 25; character set of 94 tokens (digits, letters, common symbols).

This design preserves native aspect ratios, mitigates distortion from fixed-template resizing, and better adapts to curved / vertical / multi-oriented artistic layouts.

---

Checkpoints

Each file is a standard PyTorch state_dict (~112 MB), differing only in the training data:

File	Training data	WordArt-Bench Acc.
WATERec-R.pth	WATER-R (real only, 3.2M)	88.55%
WATERec-S.pth	WATER-S (synthetic only, 2M)	80.94%
WATERec-RS.pth	WATER-R + WATER-S (real + 2M synthetic)	90.40%

WATERec-RS.pth is the recommended best model — the first result to exceed 90% on WordArt-Bench, surpassing both general-purpose and OCR-specialized VLMs by a large margin.

---

Usage

We recommend running these checkpoints with the official framework OpenOCR-WATERec, which provides the matching model configuration, preprocessing, and inference scripts.

Download the weights:

# Requires: pip install -U "huggingface_hub[cli]"
hf download Yesianrohn/WATERec-Models --local-dir ./WATERec-Models

Load a checkpoint:

import torch

# weights_only=True for safer loading of pickle-based .pth files
state_dict = torch.load("WATERec-RS.pth", map_location="cpu", weights_only=True)
# Build the WATERec model from the OpenOCR-WATERec config, then:
# model.load_state_dict(state_dict)

These .pth files contain only model weights; no config is bundled. Use the configs in the OpenOCR-WATERec repository to instantiate the architecture before loading the state dict.

---

License

Released under the Apache 2.0 license.

---

Citation

If you use these models in your research, please cite our paper:

@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}
}