---
language: ru
license: mit
task_categories:
- image-to-text
- document-question-answering
- visual-document-retrieval
size_categories:
- 1K<n<10K
annotations_creators:
- human
- machine-assisted
---

# Russian Receipts OCR (Semantic)

## Overview

This repository contains a dataset of Russian retail receipts with semantic OCR annotations.

⚠️ **Important note**  
This repository is used primarily as a **data storage and distribution location**.  
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.

---

## Description

Each sample consists of:
- a receipt image
- a corresponding JSON annotation with structured OCR information

The dataset is designed for:
- OCR and document understanding
- semantic information extraction
- evaluation of receipt parsing pipelines

---

## Dataset structure

The data is organized as a simple and explicit file-based structure:

images/
├── train/
├── validation/
└── test/

annotations/
├── train/
├── validation/
└── test/

For each image file:
images/<split>/<image_id>.jpg

there exists a corresponding annotation file:
annotations/<split>/<image_id>.json

## Annotations format

Each annotation JSON contains structured information extracted from a receipt, including:

- `seller`
- `inn`
- `date`
- `total`
- `items` (list of line items, if available)

Fields may be missing and are represented as `null`.

Bounding boxes are taken directly from OCR output and are not manually corrected.

---

## Completeness rule

A receipt is considered **complete** if:

- `total` is present  
- `date` is present  
- **either** `seller` **or** `inn` is present

## Usage notes

This dataset is intentionally stored in a **file-based format** and is consumed by custom pipelines that:

- explicitly load images from disk
- load annotation JSON files by filename
- perform OCR and semantic parsing separately

This design ensures:
- transparent data loading
- reproducible evaluation
- full control over preprocessing and inference steps

---

## Related resources

- OCR model checkpoint:  
  https://huggingface.co/SvetaLana25/dek-receipt-donut-stage1

- Evaluation and inference code:  
  Provided as part of the project submission.

---

## Disclaimer

This dataset is provided for educational and research purposes.

Sensitive information is anonymized or originates from user-provided examples.