DocLayNet

# Dataset Card for DocLayNet

## 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
- **Leaderboard:**
- **Point of Contact:**

### 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.

### Supported Tasks and Leaderboards

We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.

## Dataset Structure

### Data Fields

DocLayNet provides four types of data assets:

1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content

The COCO image record are defined like this example

```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",

// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```

The `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}
}
```

### Contributions

Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.

# DocLayNet 数据集卡片

## 目录
- [目录](#table-of-contents)
- [数据集描述](#dataset-description)
- [数据集概况](#dataset-summary)
- [支持任务与评测榜单](#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)
- [授权信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献者](#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
- **相关论文**：https://doi.org/10.1145/3534678.3539043
- **评测榜单**：
- **联络人**：

### 数据集概况

DocLayNet 提供逐页的布局分割（layout segmentation）标注真值，采用边界框（bounding-box）形式，覆盖来自6个文档类别的80863个唯一页面，包含11种不同的类别标签。相较于同类工作如 PubLayNet 或 DocBank，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，通过对每个页面布局的人工识别与解读，构建了布局分割领域的金标准（gold-standard）。
2. **丰富的布局多样性**：DocLayNet 包含来自金融、科学、专利、招标、法律文本与手册等大量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，可对布局特征进行高精度区分。
4. **冗余标注机制**：DocLayNet 中部分页面采用双标注或三标注，可用于估计标注不确定性，同时为机器学习模型的可达到预测精度提供上限参考。
5. **预定义训练、测试与验证集**：DocLayNet 提供固定的数据集划分，确保类别标签的比例均衡，避免不同划分集之间出现独特布局风格的泄露。

### 支持任务与评测榜单

我们基于DocLayNet数据集举办了ICDAR 2023竞赛，详细信息请参见https://ds4sd.github.io/icdar23-doclaynet/。

## 数据集结构

### 数据字段

DocLayNet 包含四类数据资源：
1. 所有页面的PNG图像，均调整为1025×1025像素的正方形尺寸
2. 每张PNG图像对应的COCO（Common Objects in Context）格式边界框标注
3. 额外资源：与每张PNG图像对应的单页PDF文件
4. 额外资源：与每个PDF页面对应的JSON文件，其中包含带坐标与内容的数字化文本单元格

COCO图像记录的定义如下例所示：
js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",

// 自定义字段：
"doc_category": "financial_reports" // 高级文档类别
"collection": "ann_reports_00_04_fancy", // 子集合名称
"doc_name": "NASDAQ_FFIN_2002.pdf", // 原始文档文件名
"page_no": 9, // 原始文档中的页码
"precedence": 0, // 标注顺序，若为冗余双标注或三标注则非零
},
...

其中`doc_category`字段的取值为以下常量之一：

financial_reports, // 金融报告
scientific_articles, // 科学论文
laws_and_regulations, // 法律法规
government_tenders, // 政府采购招标
manuals, // 手册
patents // 专利

### 数据划分

该数据集包含三个划分：
- `train`（训练集）
- `val`（验证集）
- `test`（测试集）

## 数据集构建

### 标注信息

#### 标注过程

用于培训标注专家的标注指南可参见[DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf)。

#### 标注人员

标注工作由众包完成。

## 附加信息

### 数据集维护者

本数据集由IBM Research旗下的[Deep Search团队](https://ds4sd.github.io/)维护。
您可通过[[e-mail]](mailto:[e-mail])与我们取得联系。

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)

### 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: 面向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 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 = {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}
}

### 贡献者

感谢[@dolfim-ibm](https://github.com/dolfim-ibm)、[@cau-git](https://github.com/cau-git)为本数据集的收录提供帮助。