metadata

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_table
      sequence:
        sequence: int64
    - name: bboxes_cell
      sequence:
        sequence:
          sequence: int64
  splits:
    - name: train
      num_bytes: 134578038
      num_examples: 1200
    - name: test
      num_bytes: 44974087
      num_examples: 390
  download_size: 162624154
  dataset_size: 179552125
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-TRACKB

This dataset is a resized version of the original cndplab-founder/ICDAR2019_cTDaR, merged with with its supplement cndplab-founder/ICDAR2019_cTDaR_dataset_supplement.

You can easily and quickly load it:

dataset = load_dataset("dvgodoy/ICDAR2019_cTDaR_TRACKB_resized")

DatasetDict({
    train: Dataset({
        features: ['image', 'width', 'height', 'category', 'label', 'bboxes_table', 'bboxes_cell'],
        num_rows: 1200
    })
    test: Dataset({
        features: ['image', 'width', 'height', 'category', 'label', 'bboxes_table', 'bboxes_cell'],
        num_rows: 390
    })
})

Dataset Description
- Dataset Summary
Dataset Structure
Additional Information
- Licensing Information

Dataset Description

Homepage: ICDAR 2019 cTDaR Dataset
Repository: GitHUb
Paper:
Leaderboard: Competition Results
Point of Contact: [email protected]

Dataset Summary

From the original ICDAR2019 cTDaR dataset 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 B" (table recognition) 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).

For the modern dataset no training data is available for Track B.

The original dataset did not contain "modern" tables or annotations for "Track B", so the supplement dataset was merged into it, and its annotations converted accordingly.

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_table': [[...]],
    'bboxes_cell': [[...]]
}

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_table: list of box coordinates in (xmin, ymin, xmax, ymax) format (Pascal VOC).
bboxes_cell: list of lists of box coordinates in (xmin, ymin, xmax, ymax) format (Pascal VOC) - the outer list matches the length of the bboxes_table list, and each of its elements is a list of cells.

Class Label Mappings

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

Data Splits

	train	test
# of examples	1200	390

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, merged with its supplement, which is licensed under BSD 2-Clause License.

Datasets:

dvgodoy
/

ICDAR2019_cTDaR_TRACKB_resized