v1.0: initial receipt dataset

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+# ===== Images (our dataset) =====
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+>>>>>>> e76baa0 (v1.0: initial receipt dataset)

README.md

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+---
+language: ru
+license: mit
+task_categories:
+- document-analysis
+- optical-character-recognition
+size_categories:
+- 1K<n<10K
+annotations_creators:
+- human
+- machine-assisted
+---
+
+# Russian Receipts OCR (Semantic)
+
+## Description
+Dataset of Russian retail receipts with semantic OCR annotations.
+Each sample consists of an image and structured fields extracted
+from OCR (seller, inn, date, total, items).
+
+## Structure
+- images/{train,validation,test}
+- annotations/{train,validation,test}.jsonl
+
+## Fields
+Each record contains:
+- image: relative image path
+- image_id: unique receipt id
+- seller, inn, date, total: dict or null
+- items: list
+- is_complete: boolean
+- receipt_quality: string
+
+## Completeness Rule
+Receipt is considered complete if:
+- total is present
+- date is present
+- seller OR inn is present
+
+`is_complete = False` is a valid training example.
+
+## Annotation Notes
+- Bounding boxes are taken directly from OCR
+- OCR text is not manually corrected
+- Missing values are represented as `null`