Hugging Face's logo

Datasets:
dvgodoy
/
rvl_cdip_mini

Tasks:
Image Classification
Object Detection
Modalities:
Image
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
1
rvl_cdip_mini / README.md
dvgodoy's picture
dvgodoy
Update README.md
74de67d verified
preview code
|
raw
history blame contribute delete
7.63 kB
metadata
language:
  - en
license: other
size_categories:
  - 1K<n<10K
task_categories:
  - image-classification
  - object-detection
paperswithcode_id: rvl-cdip
pretty_name: RVL-CDIP Mini
dataset_info:
  features:
    - name: image
      dtype: image
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: category
      dtype: string
    - name: ocr_words
      sequence: string
    - name: word_boxes
      sequence:
        sequence: int64
    - name: ocr_paragraphs
      sequence: string
    - name: paragraph_boxes
      sequence:
        sequence: int64
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 353331082.8
      num_examples: 3200
    - name: validation
      num_bytes: 43966539
      num_examples: 400
    - name: test
      num_bytes: 42947141
      num_examples: 400
  download_size: 424911398
  dataset_size: 440244762.8
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for RVL-CDIP-MINI

This dataset is a subset (1%) of the original aharley/rvl_cdip merged with the corresponding annotations from jordyvl/rvl_cdip_easyocr.

You can easily and quickly load it:

dataset = load_dataset("dvgodoy/rvl_cdip_mini")

DatasetDict({
    train: Dataset({
        features: ['image', 'width', 'height', 'category', 'ocr_words', 'word_boxes', 'ocr_paragraphs', 'paragraph_boxes', 'label'],
        num_rows: 3200
    })
    validation: Dataset({
        features: ['image', 'width', 'height', 'category', 'ocr_words', 'word_boxes', 'ocr_paragraphs', 'paragraph_boxes', 'label'],
        num_rows: 400
    })
    test: Dataset({
        features: ['image', 'width', 'height', 'category', 'ocr_words', 'word_boxes', 'ocr_paragraphs', 'paragraph_boxes', 'label'],
        num_rows: 400
    })
})

Table of Contents

Dataset Description

Dataset Summary

The original RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.

This "mini" version contains only the first 4,000 images from the original dataset: 3,200 training images, 400 validation images, and 400 test images.

Supported Tasks and Leaderboards

  • image-classification: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available here.

Languages

All the classes and documents use English as their primary language.

Dataset Structure

Data Instances

A sample from the training set is provided below :

{
    'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>,
    'width': 754,
    'height': 1000,
    'category': 'advertisement',
    'ocr_words': [...],
    'word_boxes': [[...]],
    'ocr_paragraphs': [...],
    'paragraph_boxes': [[...]],
    'label': 4
}

Data Fields

  • image: A PIL.Image.Image object containing a document.
  • width: image's width.
  • height: image's height.
  • category: class label.
  • ocr_words: list of OCRed words.
  • word_boxes: list of box coordinates in (xmin, ymin, xmax, ymax) format (Pascal VOC).
  • ocr_paragraphs: list of OCRed paragraphs.
  • paragraph_boxes: list of box coordinates in (xmin, ymin, xmax, ymax) format (Pascal VOC).
  • label: an int classification label.
Class Label Mappings 
{
  "0": "letter",
  "1": "form",
  "2": "email",
  "3": "handwritten",
  "4": "advertisement",
  "5": "scientific report",
  "6": "scientific publication",
  "7": "specification",
  "8": "file folder",
  "9": "news article",
  "10": "budget",
  "11": "invoice",
  "12": "presentation",
  "13": "questionnaire",
  "14": "resume",
  "15": "memo"
}

Data Splits

train test validation
# of examples 3200 400 400

The dataset was split in proportions similar to those of ImageNet.

  • 3200 images were used for training,
  • 400 images for validation, and
  • 400 images for testing.

Dataset Creation

Curation Rationale

From the paper:

This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis.

Source Data

Initial Data Collection and Normalization

The same as in the IIT-CDIP collection.

Who are the source language producers?

The same as in the IIT-CDIP collection.

Annotations

Annotation process

The same as in the IIT-CDIP collection.

Who are the annotators?

The same as in the IIT-CDIP collection.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.

Licensing Information

RVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.

Citation Information 

@inproceedings{harley2015icdar,
    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
    year = {2015}
}

Contributions

Thanks to @dnaveenr for adding this dataset.