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--- |
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license: mit |
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task_categories: |
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- image-to-text |
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- object-detection |
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- token-classification |
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language: |
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- id |
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- en |
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tags: |
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- receipt |
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- ocr |
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- information-extraction |
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- cord |
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- indonesian |
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size_categories: |
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- n<1K |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: ground_truth |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 7311152.0 |
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num_examples: 5 |
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download_size: 7282064 |
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dataset_size: 7311152.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# parlarlax/tiny-cord |
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CORD (Consolidated Receipt Dataset) is a dataset for receipt understanding tasks. |
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This dataset contains Indonesian restaurant receipts with structured annotations |
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for menu items, prices, and text extraction with bounding boxes. |
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## Dataset Details |
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### Dataset Description |
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The CORD dataset contains receipt images and their corresponding structured annotations. |
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Each example includes: |
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- **Receipt Image**: High-resolution image of Indonesian restaurant receipts |
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- **Menu Items**: Structured data with item names, quantities, and prices |
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- **Totals**: Subtotal, service charges, taxes, and final total |
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- **Text Annotations**: Detailed text extraction with bounding box coordinates |
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### Dataset Structure |
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```python |
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{ |
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'image': PIL.Image, |
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'image_id': int, |
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'image_size': {'width': int, 'height': int}, |
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'version': str, |
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'split': str, |
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'menu_items': [ |
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{'nm': str, 'cnt': str, 'price': str}, ... |
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], |
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'totals': { |
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'subtotal_price': str, |
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'service_price': str, |
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'tax_price': str, |
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'etc': str, |
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'total_price': str |
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}, |
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'text_annotations': [ |
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{ |
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'words': [{'text': str, 'bbox': [int, int, int, int], 'is_key': int}, ...], |
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'category': str, |
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'group_id': int, |
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'sub_group_id': int |
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}, ... |
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] |
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} |
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``` |
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### Supported Tasks |
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- **Receipt Understanding**: Extract structured information from receipt images |
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- **OCR (Optical Character Recognition)**: Text extraction with spatial information |
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- **Information Extraction**: Named entity recognition for receipt components |
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- **Document Layout Analysis**: Understanding spatial relationships in receipts |
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### Languages |
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The receipts contain text in: |
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- Indonesian (primary language) |
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- English (some menu items and labels) |
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### Dataset Statistics |
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- Number of examples: Varies based on available receipt images |
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- Image dimensions: 864 x 1296 pixels |
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- Average menu items per receipt: ~20-25 items |
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- Text annotations include bounding boxes for precise localization |
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## Dataset Creation |
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This dataset was created from receipt images and corresponding JSON annotations |
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containing ground truth information about menu items, prices, and text locations. |
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### Source Data |
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The source receipts are from Indonesian restaurants, primarily from the Bali region. |
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All prices are in Indonesian Rupiah (IDR). |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("parlarlax/tiny-cord") |
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# Access an example |
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example = dataset['train'][0] |
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image = example['image'] |
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menu_items = example['menu_items'] |
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total_price = example['totals']['total_price'] |
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``` |
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## Dataset Card Contact |
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For questions or issues regarding this dataset, please create an issue in the repository. |
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