Datasets:

AlroWilde
/

invoice-checkmark-annotations

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label invoice-checkmark-annotations@59b27098f177e455f6638b4cc87a9520a27ca039
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2674, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2208, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2241, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1172, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label invoice-checkmark-annotations@59b27098f177e455f6638b4cc87a9520a27ca039

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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! 🚀

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