DocLayNet-v1.1

# Dataset Card for DocLayNet v1.1

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043

### Dataset Summary

DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:

1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.

## Dataset Structure

This dataset is structured differently from the other repository [ds4sd/DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet), as this one includes the content (PDF cells) of the detections, and abandons the COCO format.

* `image`: page PIL image.
* `bboxes`: a list of layout bounding boxes.
* `category_id`: a list of class ids corresponding to the bounding boxes.
* `segmentation`: a list of layout segmentation polygons.
* `pdf_cells`: a list of lists corresponding to `bbox`. Each list contains the PDF cells (content) inside the bbox.
* `metadata`: page and document metadetails.

Bounding boxes classes / categories:

```
1: Caption
2: Footnote
3: Formula
4: List-item
5: Page-footer
6: Page-header
7: Picture
8: Section-header
9: Table
10: Text
11: Title
```

The `["metadata"]["doc_category"]` field uses one of the following constants:

```
* financial_reports,
* scientific_articles,
* laws_and_regulations,
* government_tenders,
* manuals,
* patents
```

### Data Splits

The dataset provides three splits
- `train`
- `val`
- `test`

## Dataset Creation

### Annotations

#### Annotation process

The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).

#### Who are the annotators?

Annotations are crowdsourced.

## Additional Information

### Dataset Curators

The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[e-mail]](mailto:[e-mail]).

Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)

### Licensing Information

License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)

### Citation Information

```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```

# DocLayNet v1.1 数据集卡片

## 目录
- [目录](#table-of-contents)
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务与基准排行榜](#supported-tasks-and-leaderboards)
- [数据集结构](#dataset-structure)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [标注信息](#annotations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献说明](#contributions)

## 数据集描述
- **主页**：https://developer.ibm.com/exchanges/data/all/doclaynet/
- **代码仓库**：https://github.com/DS4SD/DocLayNet
- **论文**：https://doi.org/10.1145/3534678.3539043

### 数据集概述
DocLayNet提供逐页布局分割的标注真值（ground truth），采用边界框（bounding box）对来自6个文档类别的80863个唯一页面上的11个不同类别标签进行标注。相较于PubLayNet或DocBank等同类数据集，它具备多项独特优势：
1. **人工标注**：DocLayNet由经过专业培训的专家手动标注，通过人工识别与解读每个页面的布局，为布局分割任务提供金标准（gold standard）标注质量。
2. **丰富的布局多样性**：DocLayNet涵盖来自金融、学术、专利、招投标、法律文本与操作手册等多类公开来源的多样化复杂布局。
3. **细粒度标签集**：DocLayNet定义了11个类别标签，可高精度区分各类布局特征。
4. **冗余标注机制**：部分页面采用双标注或三标注的方式，可用于估算标注不确定性，同时为机器学习模型可达到的预测准确率提供上限参考。
5. **预定义数据集划分**：DocLayNet提供固定的训练、验证与测试集合，确保类别标签的比例均衡，避免不同划分集合间出现独特布局样式的跨集泄露。

### 支持任务与基准排行榜

## 数据集结构
本数据集的结构与另一个仓库[ds4sd/DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)存在差异：本版本包含检测结果对应的PDF单元格内容，且摒弃了COCO格式。

* `image`：页面PIL图像（PIL image）。
* `bboxes`：布局边界框列表。
* `category_id`：与边界框对应的类别ID列表。
* `segmentation`：布局分割多边形列表。
* `pdf_cells`：与`bbox`对应的列表集合，每个子列表包含对应边界框内的PDF单元格内容。
* `metadata`：页面与文档元数据。

边界框类别/标签如下：

1: 图注（Caption）
2: 脚注（Footnote）
3: 公式（Formula）
4: 列表项（List-item）
5: 页脚（Page-footer）
6: 页头（Page-header）
7: 图片（Picture）
8: 章节标题（Section-header）
9: 表格（Table）
10: 正文（Text）
11: 标题（Title）

`["metadata"]["doc_category"]`字段使用以下常量之一：

* financial_reports（财务报告）,
* scientific_articles（学术论文）,
* laws_and_regulations（法律法规）,
* government_tenders（政府招投标文件）,
* manuals（操作手册）,
* patents（专利文档）

### 数据字段

### 数据划分
本数据集提供三类划分：
- `train`（训练集）
- `val`（验证集）
- `test`（测试集）

## 数据集构建
### 标注信息
#### 标注流程
用于培训标注专家的标注指南可在[DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf)获取。

#### 标注人员构成
标注工作采用众包形式完成。

## 附加信息
### 数据集维护者
本数据集由IBM研究院的[Deep Search团队](https://ds4sd.github.io/)维护。
可通过邮箱[[e-mail]](mailto:[e-mail])联系我们。

维护者列表：
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)

### 许可信息
许可证：[CDLA-Permissive-1.0](https://cdla.io/permissive-1.0/)

### 引用信息
bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}

### 贡献说明