---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: category
    dtype: string
  - name: label
    dtype: int64
  - name: bboxes
    sequence:
      sequence: int64
  splits:
  - name: train
    num_bytes: 124686342
    num_examples: 1200
  - name: test
    num_bytes: 47139142
    num_examples: 439
  download_size: 166410237
  dataset_size: 171825484
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: other
task_categories:
- image-classification
- object-detection
size_categories:
- 1K<n<10K
---
# Dataset Card for ICDAR2019-cTDaR-TRACKA

**This dataset is a resized version of the original [cndplab-founder/ICDAR2019_cTDaR](https://github.com/cndplab-founder/ICDAR2019_cTDaR).**

You can easily and quickly load it:

```python
dataset = load_dataset("dvgodoy/ICDAR2019_cTDaR_TRACKA_resized")
```

```
DatasetDict({
    train: Dataset({
        features: ['image', 'width', 'height', 'category', 'label', 'bboxes'],
        num_rows: 1200
    })
    test: Dataset({
        features: ['image', 'width', 'height', 'category', 'label', 'bboxes'],
        num_rows: 439
    })
})
```

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)

## Dataset Description

- **Homepage:** [ICDAR 2019 cTDaR Dataset](https://cndplab-founder.github.io/cTDaR2019/dataset-description.html)
- **Repository:** [GitHUb](https://github.com/cndplab-founder/ICDAR2019_cTDaR)
- **Paper:**
- **Leaderboard:** [Competition Results](https://cndplab-founder.github.io/cTDaR2019/results.html)
- **Point of Contact:** [cTDAR@cvl.tuwien.ac.at](mailto:cTDAR@cvl.tuwien.ac.at)

### Dataset Summary

From the original ICDAR2019 cTDaR [dataset](https://cndplab-founder.github.io/cTDaR2019/dataset-description.html) page:

> _The dataset consists of modern documents and archival ones with various formats, including document images and born-digital formats such as PDF. The annotated contents contain the table entities and cell entities in a document, while we do not deal with nested tables._

**This "resized" version contains all the images from "Track A" (table detection) resized so that the largest dimension (either width or height) is 1000px. The annotations were converted from XML to JSON and boxes are represented in Pascal VOC format `(xmin, ymin, xmax, ymax)`.**

## Dataset Structure

### Data Instances

A sample from the training set is provided below :
```
{
    'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=1000x729>,
    'width': 1000,
    'height': 729,
    'category': 'historical',
    'label': 0,
    'bboxes': [[...]]
}
```

### Data Fields

- `image`: A `PIL.Image.Image` object containing a document.
- `width`: image's width.
- `height`: image's height.
- `category`: class label.
- `label`: an `int` classification label.
- `bboxes`: list of box coordinates in `(xmin, ymin, xmax, ymax)` format (Pascal VOC).

<details>
  <summary>Class Label Mappings</summary>

```json
{
  "0": "historical",
  "1": "modern"
}
```

</details>

### Data Splits

|   |train|test|
|----------|----:|----:|
|# of examples|1200|439|

## Additional Information

### Licensing Information

This dataset is a resized and reorganized version of ICDAR2019 cTDaR from the [ICDAR 2019 Competition on Table Detection and Recognition](https://cndplab-founder.github.io/cTDaR2019/index.html).