--- 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/_/` as `__.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} } ```