metadata

license: mit
task_categories:
  - image-to-text
  - object-detection
  - token-classification
language:
  - id
  - en
tags:
  - receipt
  - ocr
  - information-extraction
  - cord
  - indonesian
size_categories:
  - n<1K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: ground_truth
      dtype: string
  splits:
    - name: train
      num_bytes: 7311152
      num_examples: 5
  download_size: 7282064
  dataset_size: 7311152
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

parlarlax/tiny-cord

CORD (Consolidated Receipt Dataset) is a dataset for receipt understanding tasks. This dataset contains Indonesian restaurant receipts with structured annotations for menu items, prices, and text extraction with bounding boxes.

Dataset Details

Dataset Description

The CORD dataset contains receipt images and their corresponding structured annotations. Each example includes:

Receipt Image: High-resolution image of Indonesian restaurant receipts
Menu Items: Structured data with item names, quantities, and prices
Totals: Subtotal, service charges, taxes, and final total
Text Annotations: Detailed text extraction with bounding box coordinates

Dataset Structure

{
    'image': PIL.Image,
    'image_id': int,
    'image_size': {'width': int, 'height': int},
    'version': str,
    'split': str,
    'menu_items': [
        {'nm': str, 'cnt': str, 'price': str}, ...
    ],
    'totals': {
        'subtotal_price': str,
        'service_price': str, 
        'tax_price': str,
        'etc': str,
        'total_price': str
    },
    'text_annotations': [
        {
            'words': [{'text': str, 'bbox': [int, int, int, int], 'is_key': int}, ...],
            'category': str,
            'group_id': int,
            'sub_group_id': int
        }, ...
    ]
}

Supported Tasks

Receipt Understanding: Extract structured information from receipt images
OCR (Optical Character Recognition): Text extraction with spatial information
Information Extraction: Named entity recognition for receipt components
Document Layout Analysis: Understanding spatial relationships in receipts

Languages

The receipts contain text in:

Indonesian (primary language)
English (some menu items and labels)

Dataset Statistics

Number of examples: Varies based on available receipt images
Image dimensions: 864 x 1296 pixels
Average menu items per receipt: ~20-25 items
Text annotations include bounding boxes for precise localization

Dataset Creation

This dataset was created from receipt images and corresponding JSON annotations containing ground truth information about menu items, prices, and text locations.

Source Data

The source receipts are from Indonesian restaurants, primarily from the Bali region. All prices are in Indonesian Rupiah (IDR).

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("parlarlax/tiny-cord")

# Access an example
example = dataset['train'][0]
image = example['image']
menu_items = example['menu_items']
total_price = example['totals']['total_price']

Dataset Card Contact

For questions or issues regarding this dataset, please create an issue in the repository.