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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...
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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.
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4×4 patch tokens lies in
[64, 256]; tokens are projected to d=384 and arranged in row-major order.This design preserves native aspect ratios, mitigates distortion from fixed-template resizing, and better adapts to curved / vertical / multi-oriented artistic layouts.
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.
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
.pthfiles 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.
Released under the Apache 2.0 license.
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}
}
pth · 107 MB · SHA-256 391b750d00ef…fd91 · Hugging Face
--- license: apache-2.0 pipeline_tag: image-to-text library_name: pytorch language: - en tags: - scene-text-recognition - STR - OCR - artistic-text - wordart - WATERec --- # 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](https://github.com/YesianRohn/OpenOCR-WATERec), which provides the matching mode...
Source context: 0 downloads · 0 likes · Pipeline image-to-text · Library pytorch · Repo Yesianrohn/WATERec-Models