| --- |
| license: apache-2.0 |
| task_categories: |
| - text-to-image |
| language: |
| - en |
| - zh |
| pretty_name: PolyText-Bench |
| size_categories: |
| - n<1K |
| tags: |
| - text-to-image |
| - visual-text-rendering |
| - benchmark |
| - multimodal |
| - textdart |
| - scanner |
| dataset_info: |
| features: |
| - name: pair_id |
| dtype: string |
| - name: task_type |
| dtype: string |
| - name: language |
| dtype: string |
| - name: scene_category |
| dtype: string |
| - name: prompt |
| dtype: string |
| - name: target_list |
| list: string |
| - name: target |
| dtype: string |
| splits: |
| - name: critical_en |
| num_examples: 150 |
| - name: optional_en |
| num_examples: 150 |
| - name: critical_zh |
| num_examples: 150 |
| - name: optional_zh |
| num_examples: 150 |
| configs: |
| - config_name: default |
| data_files: |
| - split: critical_en |
| path: critical_en.jsonl |
| - split: optional_en |
| path: optional_en.jsonl |
| - split: critical_zh |
| path: critical_zh.jsonl |
| - split: optional_zh |
| path: optional_zh.jsonl |
| --- |
| |
| # PolyText-Bench |
|
|
| > [**Project Page**](https://sujiehu.github.io/textdart-scanner/) |
|
|
| PolyText-Bench is a bilingual benchmark for evaluating text accuracy and glyph fidelity |
| in text-to-image generation. It is introduced in |
| [Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation](https://arxiv.org/abs/2610.20794). |
|
|
| This repository contains both the **600 benchmark prompts** and the scripts required for |
| image generation, TextDART deployment, resumable scoring, and metric aggregation. |
|
|
| ## Benchmark Design |
|
|
| PolyText-Bench contains paired critical and optional prompts in English and Chinese: |
|
|
| - **Critical prompts** specify one or more required text targets. They measure Critical |
| Semantic accuracy (**CS**) and Critical Glyph quality (**CG**). |
| - **Optional prompts** describe the same scenes without prescribing text. They measure |
| Optional Glyph quality (**OG**) over text that the generator introduces voluntarily. |
|
|
| | Split | Language | Task | Prompts | |
| |---|---|---|---:| |
| | `critical_en` | English | Required target text | 150 | |
| | `optional_en` | English | No required target text | 150 | |
| | `critical_zh` | Chinese | Required target text | 150 | |
| | `optional_zh` | Chinese | No required target text | 150 | |
|
|
| Critical and optional prompts with the same `pair_id` describe matched scenes. The |
| provided evaluation protocol generates four images per prompt by default using |
| deterministic paired seeds. |
|
|
| ## Repository Contents |
|
|
| ```text |
| PolyText-Bench/ |
| ├── critical_en.jsonl |
| ├── optional_en.jsonl |
| ├── critical_zh.jsonl |
| ├── optional_zh.jsonl |
| ├── generate.py |
| ├── deploy_textdart.sh |
| ├── evaluate.py |
| ├── summarize.py |
| ├── requirements.txt |
| └── LICENSE |
| ``` |
|
|
| ## Load the Prompts |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("SujieHu/PolyText-Bench", "default") |
| critical_en = dataset["critical_en"] |
| optional_en = dataset["optional_en"] |
| critical_zh = dataset["critical_zh"] |
| optional_zh = dataset["optional_zh"] |
| ``` |
|
|
| Each record contains: |
|
|
| - `pair_id`: shared identifier for a critical/optional scene pair |
| - `task_type`: `critical` or `optional` |
| - `language`: `en` or `zh` |
| - `scene_category`: visual scene category |
| - `prompt`: text-to-image generation prompt |
| - `target_list`: required rendered strings; empty for optional prompts |
| - `target`: space-joined convenience representation of `target_list` |
|
|
| ## Installation |
|
|
| Create the TextDART environment using the official source repository: |
|
|
| ```bash |
| conda create -n TextDART python=3.11 -y |
| conda activate TextDART |
| git clone https://github.com/WeChatCV/TextDART-SCANNER.git |
| cd TextDART-SCANNER/TextDART/train |
| bash install_all.sh |
| cd ../.. |
| ``` |
|
|
| Then clone this Dataset repository and install its scoring dependency: |
|
|
| ```bash |
| git clone https://huggingface.co/datasets/SujieHu/PolyText-Bench |
| cd PolyText-Bench |
| pip install -r requirements.txt |
| ``` |
|
|
| The evaluation code uses the official |
| [WeChatCV/TextDART-SCANNER](https://github.com/WeChatCV/TextDART-SCANNER) |
| implementation for TextDART parsing and target matching. |
|
|
| ## Download and Serve TextDART |
|
|
| Passing the Hugging Face repository ID downloads the model |
| automatically: |
|
|
| ```bash |
| bash deploy_textdart.sh SujieHu/TextDART-8B-Qwen3VL 1925 0 |
| ``` |
|
|
| To download the checkpoint first and serve it from disk instead: |
|
|
| ```bash |
| hf download SujieHu/TextDART-8B-Qwen3VL \ |
| --local-dir TextDART-8B-Qwen3VL |
| bash deploy_textdart.sh ./TextDART-8B-Qwen3VL 1925 0 |
| ``` |
|
|
| ## Generate Images |
|
|
| Generate English images with Qwen-Image: |
|
|
| ```bash |
| torchrun --nproc_per_node=8 generate.py \ |
| --model-family qwen \ |
| --mode EN \ |
| --model_name qwen_image \ |
| --model_path Qwen/Qwen-Image-2512 \ |
| --gallery_output_dir outputs |
| ``` |
|
|
| For FLUX.1-dev, set `--model-family flux` and provide its model path. Add |
| `--ckpt_path /path/to/adapter` to evaluate a PEFT LoRA checkpoint. Generation defaults |
| to 1024 × 1024, 50 steps, and four repeats per prompt. |
|
|
| Generated files are written to `outputs/<model_name>_<LANG>/` as |
| `<pair_id>_<task_type>_<repeat>.png`. You may also evaluate externally generated images |
| if they follow this naming and resolution contract. |
|
|
| ## Evaluate |
|
|
| With the TextDART server running: |
|
|
| ```bash |
| python evaluate.py \ |
| --mode EN \ |
| --image-dir outputs/qwen_image_EN \ |
| --output-dir scores/qwen_image_EN \ |
| --server-urls http://127.0.0.1:1925 |
| ``` |
|
|
| The evaluator validates every expected image, scores it with TextDART, writes each |
| successful result durably, and resumes from `eval_results.jsonl` after interruption. |
|
|
| Validate score completeness and write `summary.json` plus `scores.md`: |
|
|
| ```bash |
| python summarize.py \ |
| --mode EN \ |
| --scores-dir scores/qwen_image_EN |
| ``` |
|
|
| ## Metrics |
|
|
| - **CS (Critical Semantic accuracy):** mean semantic score over required text targets; |
| unmatched targets receive zero. |
| - **CG (Critical Glyph quality):** mean glyph-quality score over required text targets; |
| unmatched targets receive zero. |
| - **OG (Optional Glyph quality):** mean glyph quality over text regions detected in |
| optional-prompt images; images without detected text are reported as undefined and |
| excluded from the OG average. |
|
|
| All reported scores are normalized to `[0, 1]`; higher is better. |
|
|
| ## Citation |
|
|
| If you find PolyText-Bench useful, please cite: |
|
|
| ```bibtex |
| @article{hu2026textdartscanner, |
| title={Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation}, |
| author={Hu, Sujie and Zhang, Huaisong and Yang, Pujing and Wang, Qian and Li, Xiaomin and Leng, Xinhang and Li, Chen and Lyu, Jing and Li, Xiu and Pan, Ling}, |
| journal={arXiv preprint arXiv:2610.20794}, |
| year={2026} |
| } |
| ``` |
|
|
