Datasets:

AlroWilde
/

invoice-checkmark-annotations

language: multilingual
license: apache-2.0
tags:
  - invoice
  - key-information-extraction
  - kie
  - document-understanding
  - ocr
  - visual-annotation
  - checkmarks
  - layout-analysis
  - ukrainian
  - chinese
  - swedish
size_categories:
  - 600 images

Invoice Checkmark Annotations

Multilingual dataset of real invoices with human-drawn visual checkmarks/circles indicating verified key fields.

This dataset contains ~600 annotated invoice images (≈200 per language) in Ukrainian, Chinese, and Swedish. Each image shows real-world invoices where a human has manually added checkmarks (✓) or circles to highlight correctly extracted or verified fields (e.g. invoice number, buyer name, line totals, tax rate).

Every sample includes:

The original scanned/photographed invoice image (with visible pen/circle markings)
A JSON annotation file with:
- file_name: path to the image
- data: list of extracted fields, each with:
  - field: field name (e.g. "Unique Invoice Identifier", "Vendor Business Address", "Customer/Buyer Name", "Invoice Table Row 1: Line Total Amount", "Applied Tax Percentage")
  - checked: boolean (true if the field was marked)
  - text: the extracted text string

Example annotation snippet:

{
  "file_name": "UK/187.jpeg",
  "data": [
    {"field": "Unique Invoice Identifier", "checked": true, "text": "#213253"},
    {"field": "Vendor Business Address", "checked": true, "text": "Аллея Беринга 494"},
    {"field": "Customer/Buyer Name", "checked": true, "text": "Владилена Басок"},
    {"field": "Invoice Table Row 1: Line Total Amount", "checked": true, "text": "1,314.17 грн"},
    {"field": "Applied Tax Percentage", "checked": true, "text": "15"}
  ]
}

Why this dataset? Current public invoice datasets (e.g. FATURA, SROIE, CORD, etc.) focus mainly on clean text extraction or layout parsing. This is (to our knowledge) the first public dataset that includes explicit visual human verification signals — checkmarks and circles drawn directly on the invoice images. These visual cues are extremely valuable for training next-generation Document AI / VLM / KIE models that need to:

Understand human feedback/confirmation signals Detect visual annotations (underlines, circles, ticks) Improve reliability in high-stakes invoice processing (finance, logistics, auditing)

The idea was inspired by discussions on visual marking detection in complex documents (see the Hacker News thread on GLM-OCR, where users highlighted the need for better handling of pen/pencil marks like checkmarks in contract/invoice analysis pipelines).

Languages & Size

Ukrainian: ~200 images (UAH currency, Cyrillic addresses, typical UA invoice layouts)
Chinese: ~200 images
Swedish: ~200 images

Total: ≈600 images + corresponding JSON annotations.
Structure
textinvoice-checkmark-annotations/
├── Ukrainian/
│   ├── 001.jpeg
│   ├── 002.jpeg
│   ├── ...
│   └── label.txt
├── Chinese/
│   └── ...
├── Swedish/
│   └── ...
└── README.md

(You can load it easily with datasets.load_dataset("AlroWilde/invoice-checkmark-annotations") — split by language subfolders or add a language column if you prefer a flat/parquet structure later.) License Apache License 2.0 — feel free to use, modify, and build commercial models on top of this dataset. Attribution is appreciated but not required.

Related Project

This dataset pairs well with ocr-producer - a synthetic generator focused on documents.

Use real + synthetic data together with this checkmark-annotated set to train more robust KIE / visual-verification models.

Contact Questions, collaborations, other language support, or bug reports? Reach out at hi@support.alrowilde.com

Happy training! 🚀