Datasets:

ATH-MaaS
/

TextRenderBench-ZH

language:
  - zh
license: apache-2.0
task_categories:
  - image-classification
pretty_name: TextRenderBench-ZH
size_categories:
  - 1K<n<10K
configs:
  - config_name: images
    data_files:
      - split: test
        path: data/images/test-*.parquet
  - config_name: annotations
    data_files:
      - split: test
        path: data/annotations/test-*.parquet

TextRenderBench-ZH

TextRenderBench-ZH is an evaluation-only benchmark for target-conditioned verification of structural Chinese glyph rendering. Text-to-image models increasingly produce Chinese text that is readable yet locally wrong, and recognition-oriented systems are invariant to exactly those deviations. This benchmark labels each character crop with its position, intended character, and human-confirmed structural correctness, so detectors can be compared on a shared per-character protocol.

The test split holds 299 anonymous source images and 7,127 character-level annotations, of which 608 are rendering errors. There is no training split.

Resources

Resource	Location
Paper	TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering (arXiv:2610.19551)
Code	github.com/ATH-MaaS/TextVerifier
Model weights	ATH-MaaS/TextVerifier-CJK-CLIP
This dataset	ATH-MaaS/TextRenderBench-ZH

Dataset configurations

`images`

The test split contains 299 rows:

image_id: anonymous image identifier.
image: embedded RGB PNG image.
image_path: canonical relative path used by the TextVerifier evaluator.
width, height: image dimensions; every image is 1024 × 1024.

`annotations`

The test split contains 7,127 rows:

sample_id: unique character-level sample identifier.
image_id: foreign key into the images configuration.
image_path: canonical relative image path.
item_index: annotation index within the source image.
char: intended Chinese character.
box: four quadrilateral corner points.
rect: axis-aligned [x0, y0, x1, y1] crop rectangle.
render_error: human-confirmed structural rendering-error label.

Join the configurations on image_id.

Labeling protocol

Annotations were initialized from automatic detection and recognition results, then reviewed by human annotators for box placement, character identity, and glyph correctness. A stroke- or component-level deviation is labeled render_error; differences of font, calligraphic style, color, and background are not errors on their own.

Loading

from datasets import load_dataset

images = load_dataset(
    "ATH-MaaS/TextRenderBench-ZH",
    "images",
    split="test",
)
annotations = load_dataset(
    "ATH-MaaS/TextRenderBench-ZH",
    "annotations",
    split="test",
)

For reproducible evaluation, pin the Dataset repository to an immutable commit revision with the revision argument.

TextVerifier evaluation

The TextVerifier code repository provides scripts/materialize_benchmark.py, which converts these two Parquet configurations into the local PNG and JSONL layout expected by its evaluator.

The evaluation crop rule is:

crop = image.crop((int(x0), int(y0), int(round(x1)), int(round(y1))))

Intended use and limitations

Use this benchmark to evaluate whether a rendered Chinese glyph has a structural error conditioned on its intended character. It is an evaluation set, not a training split. The labels are imbalanced, so report precision, recall, and F1 rather than accuracy alone. Image identifiers are anonymous and must not be interpreted as source identifiers.

Citation

@article{chen2026textverifier,
  title   = {TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering},
  author  = {Chen, Yiyang and Wang, Guo-Hua and Liu, Yaoli and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Ma, Xiujun},
  journal = {arXiv preprint arXiv:2610.19551},
  year    = {2026},
  url     = {https://arxiv.org/abs/2610.19551}
}

License

The benchmark images and annotations are released under the Apache License 2.0. See LICENSE.