docling-DocLayNet-v1.1

# Dataset Card for DocLayNet v1.2

## 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

This dataset is an extention of the [original DocLayNet dataset](https://github.com/DS4SD/DocLayNet) which embeds the PDF files of the document images inside a binary column.

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.
* `area`: Area of the bboxes.
* `pdf_cells`: a list of lists corresponding to `bbox`. Each list contains the PDF cells (content) inside the bbox.
* `metadata`: page and document metadetails.
* `pdf`: Binary blob with the original PDF image.

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 v1.2 数据集卡片

## 数据集说明
- **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
本数据集是[原始DocLayNet数据集](https://github.com/DS4SD/DocLayNet)的扩展版本，将文档图像对应的PDF文件嵌入至二进制列中。

DocLayNet提供逐页的布局分割真值标注，针对6个文档类别下的80863个唯一页面，使用边界框（bounding box）为11种不同类别标签提供标注。相比PubLayNet、DocBank等同类研究工作，其具备多项独特优势：
1. **人工标注**：DocLayNet由经过专业培训的专家手工标注，通过对每一页布局的人工识别与解读，构建了文档布局分割任务的金标准标注集。
2. **丰富的布局多样性**：DocLayNet涵盖来自金融、学术、专利、招标、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. **精细的标签集**：DocLayNet定义了11个类别标签，可对布局特征进行高精度区分。
4. **冗余标注**：DocLayNet中部分页面经过双标注或三标注，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精度上限提供参考依据。
5. **预定义划分集**：DocLayNet提供固定的训练、验证与测试集划分，确保各类标签的比例均衡，避免不同划分集之间出现独特布局样式的数据泄露。

## 数据集结构
本数据集的结构与另一仓库[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，并弃用了COCO（Common Objects in Context）格式。

各字段说明如下：
* `image`：页面的PIL（Python Imaging Library）图像。
* `bboxes`：布局边界框列表。
* `category_id`：与边界框一一对应的类别ID列表。
* `segmentation`：布局分割多边形列表。
* `area`：边界框的面积。
* `pdf_cells`：与`bbox`结构对应的列表集合，每个子列表包含对应边界框内的PDF单元格内容。
* `metadata`：页面与文档的元数据详情。
* `pdf`：包含原始PDF文件的二进制大对象（Blob）。

边界框类别/标签对应关系如下：

1: 图表说明（Caption）
2: 脚注（Footnote）
3: 公式（Formula）
4: 列表项（List-item）
5: 页脚（Page-footer）
6: 页眉（Page-header）
7: 图片（Picture）
8: 章节标题（Section-header）
9: 表格（Table）
10: 正文文本（Text）
11: 文档标题（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 → 政府招标
* manuals → 操作手册
* patents → 专利

### 数据划分
本数据集提供三种划分方式：
- `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)下载获取。

#### 标注人员
本数据集的标注工作通过众包方式完成。

## 补充信息
### 数据集管理方
本数据集由IBM研究院Deep Search团队整理维护。您可通过邮箱`[e-mail]`联系我们。

管理团队成员：
- 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)

### 授权信息
授权协议：[CDLA-Permissive-1.0](https://cdla.io/permissive-1.0/)

### 引用信息
bib
@article{doclaynet2022,
title = {DocLayNet: 用于文档布局分割的大规模人工标注数据集},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit 与 Auer, Christoph 与 Dolfi, Michele 与 Nassar, Ahmed S 与 Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {美国计算机协会（Association for Computing Machinery)},
address = {美国纽约州纽约市},
booktitle = {第28届ACM SIGKDD知识发现与数据挖掘大会论文集},
pages = {3743–3751},
numpages = {9},
location = {美国华盛顿特区},
series = {KDD '22}
}