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--- |
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language: ru |
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license: mit |
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task_categories: |
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- image-to-text |
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- document-question-answering |
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- visual-document-retrieval |
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size_categories: |
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- 1K<n<10K |
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annotations_creators: |
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- human |
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- machine-assisted |
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--- |
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# Russian Receipts OCR (Semantic) |
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## Overview |
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This repository contains a dataset of Russian retail receipts with semantic OCR annotations. |
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⚠️ **Important note** |
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This repository is used primarily as a **data storage and distribution location**. |
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The dataset is consumed by **external inference, evaluation and training pipelines** and is **not intended to be loaded directly via `datasets.load_dataset()`** without a custom loader. |
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## Description |
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Each sample consists of: |
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- a receipt image |
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- a corresponding JSON annotation with structured OCR information |
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The dataset is designed for: |
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- OCR and document understanding |
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- semantic information extraction |
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- evaluation of receipt parsing pipelines |
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## Dataset structure |
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The data is organized as a simple and explicit file-based structure: |
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images/ |
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├── train/ |
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├── validation/ |
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└── test/ |
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annotations/ |
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├── train/ |
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├── validation/ |
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└── test/ |
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For each image file: |
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images/<split>/<image_id>.jpg |
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there exists a corresponding annotation file: |
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annotations/<split>/<image_id>.json |
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## Annotations format |
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Each annotation JSON contains structured information extracted from a receipt, including: |
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- `seller` |
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- `inn` |
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- `date` |
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- `total` |
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- `items` (list of line items, if available) |
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Fields may be missing and are represented as `null`. |
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Bounding boxes are taken directly from OCR output and are not manually corrected. |
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--- |
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## Completeness rule |
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A receipt is considered **complete** if: |
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- `total` is present |
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- `date` is present |
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- **either** `seller` **or** `inn` is present |
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## Usage notes |
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This dataset is intentionally stored in a **file-based format** and is consumed by custom pipelines that: |
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- explicitly load images from disk |
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- load annotation JSON files by filename |
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- perform OCR and semantic parsing separately |
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This design ensures: |
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- transparent data loading |
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- reproducible evaluation |
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- full control over preprocessing and inference steps |
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--- |
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## Related resources |
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- OCR model checkpoint: |
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https://huggingface.co/SvetaLana25/dek-receipt-donut-stage1 |
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- Evaluation and inference code: |
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Provided as part of the project submission. |
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--- |
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## Disclaimer |
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This dataset is provided for educational and research purposes. |
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Sensitive information is anonymized or originates from user-provided examples. |
