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README.md

@@ -15,110 +15,62 @@ size_categories:
 pretty_name: OmniDoc-TokenBench
 ---
 
-# OmniDoc-TokenBench
+<div align="center">
 
-Technical report and dataset are coming soon.
+<h2>OmniDoc-TokenBench</h2>
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+[![GitHub](https://img.shields.io/badge/GitHub-OmniDoc--TokenBench-181717?logo=github)](https://github.com/alibaba/OmniDoc-TokenBench)
+[![arXiv](https://img.shields.io/badge/arXiv-QwenImageVAE2.0-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2605.10730)
+
+</div>
 
 ---
 
-## Introduction
+## 📄 Overview
+
+### Introduction
 
 **OmniDoc-TokenBench** is a curated benchmark of ~3K text-rich document images for evaluating VAE reconstruction on textual content, alongside an evaluation toolkit supporting PSNR, SSIM, LPIPS, FID, and OCR-based NED metrics. It spans nine categories (*book*, *slides*, *color textbook*, *exam paper*, *academic paper*, *magazine*, *financial report*, *newspaper*, *note*) in both English and Chinese.
 
 <p align="center">
-  <img src="assets/bench.png" alt="OmniDoc-TokenBench" style="max-width: 1328px; width: 100%; height: auto;" />
+  <img src="assets/bench.png" alt="OmniDoc-TokenBench" width="80%" />
 <p>
 
-Derived from OmniDocBench, each sample is cropped from a text block and resized to 256x256 with reference character sizes of 16px (Chinese) and 10px (English). We filter for sufficient character density ([200, 600] for Chinese, [300, 600] for English), deduplicate via n-gram overlap, and manually inspect for quality.
+We develop OmniDoc-TokenBench based on [OmniDocBench](https://github.com/opendatalab/OmniDocBench). We crop each sample from a text block and resize it to 256×256, then filter for a character count range ([200, 600] for Chinese, [300, 600] for English) to ensure a reference font size of approximately 16px and 10px, respectively. We deduplicate via n-gram overlap and manually inspect for quality.
+
+
+### Evaluation Metric
 
-**Evaluation.** Beyond standard traditional metrics (PSNR, SSIM, LPIPS, FID), we use **NED** (Normalized Edit Distance) as the primary text-fidelity metric. NED compares the OCR outputs of the original and reconstructed images using Levenshtein distance:
+Beyond traditional metrics (PSNR, SSIM, LPIPS, FID), we use **NED** (Normalized Edit Distance) as the primary text-fidelity metric. NED directly measures text preservation by comparing recognized character sequences between original and reconstructed images using Levenshtein distance:
 
 $$
 \mathrm{NED} = \frac{1}{N}\sum_{i=1}^{N}\left(1 - \frac{d_{\mathrm{edit}}(s_{\mathrm{gt}}^{(i)}, s_{\mathrm{recon}}^{(i)})}{\max(|s_{\mathrm{gt}}^{(i)}|, |s_{\mathrm{recon}}^{(i)}|)}\right)
 $$
 
----
-
-## Results
-
-We conduct a comprehensive evaluation on OmniDoc-TokenBench (~3K text-rich images, 256×256). Models are grouped by spatial compression factor and sorted by NED within each group.
-
-<p align="center">
-  <img src="assets/results.png" alt="Eval-Results" style="max-width: 1024px; width: 100%; height: auto;" />
-<p>
+NED is sensitive to semantic corruption such as character substitutions, making it a necessary complementary metric when traditional metrics alone are insufficient.
 
 ---
 
-## Evaluation
-
-### Installation
-
-```bash
-pip install torch torchvision piq lpips pytorch-fid pillow numpy tqdm
-pip install paddleocr python-Levenshtein  # required for NED
-```
-
-### Usage
+## 📊 Performance
 
-Place your ground-truth images in `gt_dir/` and reconstructed images in `recon_dir/` (filenames must match one-to-one).
+We conduct a comprehensive evaluation on OmniDoc-TokenBench (~3K text-rich images, 256×256 resolution). Models are grouped by spatial compression factor and sorted by NED within each group.
 
-```bash
-# Compute NED only (default)
-python eval_metrics.py --gt_dir ./gt_dir --recon_dir ./recon_dir
+<p align="center">
+  <img src="assets/results.png" alt="Eval-Results" width="65%" />
+</p>
 
-# Compute traditional metrics (PSNR / SSIM / LPIPS / FID)
-python eval_metrics.py --gt_dir ./gt_dir --recon_dir ./recon_dir --mode pixel
+Our Qwen-Image-VAE-2.0 achieves state-of-the-art reconstruction across all compression ratios. The f16c128 variant attains SSIM **0.9706** and PSNR **30.45 dB**, surpassing the best f8 baseline (FLUX.1-dev at 0.9364 / 26.24 dB) despite 2× higher spatial compression. In terms of text fidelity (NED), f16c128 reaches **0.9617**, exceeding all evaluated VAEs. Even under extreme f32 compression, our f32c192 achieves NED **0.8555**, surpassing multiple f16 baselines.
 
-# Compute all metrics
-python eval_metrics.py --gt_dir ./gt_dir --recon_dir ./recon_dir --mode all
+---
 
-# Specify output directory and device
-python eval_metrics.py --gt_dir ./gt_dir --recon_dir ./recon_dir --save_path ./results --device cuda
-```
+## ⚡ Evaluation
 
-### Output
-
-The script writes results to the `--save_path` directory (default: `./eval_results`):
-
-- `results.json` --- Aggregated metrics (example):
-  ```json
-  {
-    "num_samples": 100,
-    "PSNR": 30.45,
-    "SSIM": 0.9706,
-    "LPIPS": 0.0523,
-    "FID": 1.98,
-    "NED": 0.9617,
-    "NED_samples": 98
-  }
-  ```
-
-- `ned_details.json` --- Per-image OCR results and NED scores (generated when `--mode` is `ned` or `all`):
-  ```json
-  {
-    "avg_ned": 0.9617,
-    "total_samples": 100,
-    "valid_samples": 98,
-    "details": [
-      {
-        "file": "0001.png",
-        "gt_ocr": "The quick brown fox...",
-        "recon_ocr": "The quick brown fox...",
-        "ned": 0.9764
-      }
-    ]
-  }
-  ```
-
-### Notes
-
-- `FID` and `LPIPS` require downloading model checkpoints on the first run (InceptionV3 ~90MB for FID, VGG16 ~530MB for LPIPS). Ensure network access or pre-download the weight files.
-- PaddleOCR defaults to CPU inference. For large-scale evaluation, consider switching to GPU by setting `device="gpu"` in `compute_ned()`.
-- The progress bar for PSNR/SSIM/LPIPS displays running means in real time.
+> For evaluation scripts and usage, see our [GitHub Repository](https://github.com/alibaba/OmniDoc-TokenBench)
 
 ---
 
-## Citation
+## 📝 Citation
 
 If you use OmniDoc-TokenBench or this evaluation toolkit in your research, please cite:
 
@@ -128,8 +80,10 @@ If you use OmniDoc-TokenBench or this evaluation toolkit in your research, pleas
 
 ---
 
-## License
+## ❤️ Acknowledgements
+
+OmniDoc-TokenBench is a derivative dataset based on [OmniDocBench](https://github.com/opendatalab/OmniDocBench), Thanks for their great work.
 
-- OmniDoc-TokenBench is a derivative dataset based on [OmniDocBench](https://github.com/opendatalab/OmniDocBench), developed by the Qwen Team at Alibaba Group.
+## 📜 License
 
-- This project is licensed under the [Apache License 2.0](LICENSE).
+This dataset is developed by the Qwen Team at Alibaba Group, and licensed under the [Apache License 2.0](LICENSE).