first submit.

README.md

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----
-license: apache-2.0
----
+
+---
+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:
+```json
+{
+  "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](https://news.ycombinator.com/item?id=46924075), where users highlighted the need for better handling of pen/pencil marks like checkmarks in contract/invoice analysis pipelines).
+
+Languages & Size
+
+```json
+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](https://github.com/alrowilde/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! 🚀