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README.md

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+---
+language:
+- zh
+license: apache-2.0
+task_categories:
+- image-to-image
+pretty_name: TextRefinementBench
+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
+---
+
+<div align="center">
+
+# TextRefinementBench
+
+[![arXiv](https://img.shields.io/badge/arXiv-2610.18284-b31b1b.svg)](https://arxiv.org/abs/2610.18284)
+[![License](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](LICENSE)
+
+**English** | [简体中文](README.zh-CN.md)
+
+</div>
+
+TextRefinementBench is a benchmark for glyph-conditioned local text refinement. Every item is a full-resolution image paired with the text regions that should be re-rendered: each target carries the intended text, the polygon of that region, and its axis-aligned box in original-image pixels. A model is given the image and the targets and has to regenerate those regions in place, so the benchmark measures both whether the intended text comes out correct and complete and whether everything outside the edited regions survives untouched.
+
+The `test` split holds 960 images and 2,357 target regions.
+
+## Dataset configurations
+
+### `images`
+
+The `test` split contains 960 rows:
+
+- `image_id`: image identifier, also the sample name used across the annotation rows.
+- `image`: embedded RGB PNG image, byte-identical to the source file.
+- `image_path`: canonical relative path, `images/<image_id>.png`.
+- `width`, `height`: image dimensions.
+
+### `annotations`
+
+The `test` split contains 2,357 rows, one per target region:
+
+- `sample_id`: unique target-level identifier, `<image_id>-<item_index>`.
+- `image_id`: foreign key into the `images` configuration.
+- `image_path`: canonical relative image path.
+- `item_index`: index of the target within its image, in annotation order.
+- `text`: the text that region should contain.
+- `box`: four polygon corner points `[x, y]` in original-image pixels.
+- `rect`: axis-aligned `[x0, y0, x1, y1]` crop rectangle in original-image pixels.
+- `annotation_status`: how the targets of that image were produced, either `existing_human_annotation` (167 images) or `reviewed_miner_proposals` (793 images).
+
+Join the two configurations on `image_id`.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+images = load_dataset(
+    "parquet",
+    data_files={"test": "data/images/test-00000-of-00001.parquet"},
+    split="test",
+)
+annotations = load_dataset(
+    "parquet",
+    data_files={"test": "data/annotations/test-00000-of-00001.parquet"},
+    split="test",
+)
+```
+
+Group the annotation rows by `image_id` to recover the ordered target list of each image.
+
+## Running a refinement model
+
+Each annotation row is one edit request. With the PixText inference interface, the targets of a single image become a list of edits:
+
+```python
+from pixtext import PixTextPipeline, TextEdit
+
+pipeline = PixTextPipeline.from_pretrained("models/PixText-MeanFlow")
+edits = [TextEdit(bbox=tuple(row["rect"]), text=row["text"]) for row in image_rows]
+result = pipeline.edit("images/sample_000001.png", edits=edits, seed=42)
+```
+
+Boxes are half-open intervals in original-image pixel coordinates and targets are processed in annotation order. Pixels outside the edited footprints are expected to be preserved exactly, so preservation is part of the measurement.
+
+## License
+
+The benchmark images and annotations are released under the Apache License 2.0. See `LICENSE`.

README.zh-CN.md

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+---
+language:
+- zh
+license: apache-2.0
+task_categories:
+- image-to-image
+pretty_name: TextRefinementBench
+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
+---
+
+<div align="center">
+
+# TextRefinementBench
+
+[![arXiv](https://img.shields.io/badge/arXiv-2610.18284-b31b1b.svg)](https://arxiv.org/abs/2610.18284)
+[![License](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](LICENSE)
+
+[English](README.md) | **简体中文**
+
+</div>
+
+TextRefinementBench 是用于字形条件局部文字修复（glyph-conditioned local text refinement）的评测基准。每个样本是一张原始分辨率图片，配上一组需要重绘的文字区域：每个目标包含目标文字、该区域的多边形，以及原图像素坐标下的轴对齐编辑框。模型拿到图片和目标后，需要在原位置重绘这些区域，因此这个基准既衡量目标文字是否被正确、完整地生成，也衡量编辑区域之外的画面是否被完整保留。
+
+`test` split 包含 960 张图片和 2,357 个目标区域。
+
+## 数据集配置
+
+### `images`
+
+`test` split 共 960 行：
+
+- `image_id`：图片标识，同时也是标注行中引用的样本名。
+- `image`：内嵌的 RGB PNG 图片，与源文件逐字节一致。
+- `image_path`：规范的相对路径，`images/<image_id>.png`。
+- `width`、`height`：图片尺寸。
+
+### `annotations`
+
+`test` split 共 2,357 行，每个目标区域一行：
+
+- `sample_id`：目标级别的唯一标识，`<image_id>-<item_index>`。
+- `image_id`：指向 `images` 配置的外键。
+- `image_path`：规范的相对图片路径。
+- `item_index`：该目标在所属图片内的序号，按标注顺序排列。
+- `text`：该区域应包含的文字。
+- `box`：原图像素坐标下的四个多边形角点 `[x, y]`。
+- `rect`：原图像素坐标下的轴对齐裁剪框 `[x0, y0, x1, y1]`。
+- `annotation_status`：该图片目标的标注来源，取值为 `existing_human_annotation`（167 张）或 `reviewed_miner_proposals`（793 张）。
+
+两个配置通过 `image_id` 关联。
+
+## 加载
+
+```python
+from datasets import load_dataset
+
+images = load_dataset(
+    "parquet",
+    data_files={"test": "data/images/test-00000-of-00001.parquet"},
+    split="test",
+)
+annotations = load_dataset(
+    "parquet",
+    data_files={"test": "data/annotations/test-00000-of-00001.parquet"},
+    split="test",
+)
+```
+
+按 `image_id` 分组标注行，即可还原每张图片按顺序排列的目标列表。
+
+## 运行修复模型
+
+每条标注行对应一次编辑请求。使用 PixText 推理接口时，单张图片的目标可直接转成编辑列表：
+
+```python
+from pixtext import PixTextPipeline, TextEdit
+
+pipeline = PixTextPipeline.from_pretrained("models/PixText-MeanFlow")
+edits = [TextEdit(bbox=tuple(row["rect"]), text=row["text"]) for row in image_rows]
+result = pipeline.edit("images/sample_000001.png", edits=edits, seed=42)
+```
+
+编辑框使用原图像素坐标，采用左闭右开的区间；目标按标注顺序处理。评测要求实际编辑范围之外的像素保持完全一致，因此区域外的像素保留情况也是评测的一部分。
+
+## 许可
+
+本基准的图片与标注以 Apache License 2.0 发布，见 `LICENSE`。

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