PolyText-Bench

Project Page

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.

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

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

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:

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:

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 implementation for TextDART parsing and target matching.

Download and Serve TextDART

Passing the Hugging Face repository ID downloads the model automatically:

bash deploy_textdart.sh SujieHu/TextDART-8B-Qwen3VL 1925 0

To download the checkpoint first and serve it from disk instead:

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:

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:

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:

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:

@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}
}