| --- |
| 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](https://arxiv.org/abs/2610.19551) (arXiv:2610.19551) | |
| | Code | [github.com/ATH-MaaS/TextVerifier](https://github.com/ATH-MaaS/TextVerifier) | |
| | Model weights | [ATH-MaaS/TextVerifier-CJK-CLIP](https://huggingface.co/ATH-MaaS/TextVerifier-CJK-CLIP) | |
| | This dataset | [ATH-MaaS/TextRenderBench-ZH](https://huggingface.co/datasets/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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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`. |
