| --- |
| license: cc-by-4.0 |
| pretty_name: CORD (receipts) — OCR + key-information-extraction ground truth |
| task_categories: |
| - image-to-text |
| - object-detection |
| language: |
| - en |
| - id |
| size_categories: |
| - n<1K |
| tags: |
| - cord |
| - ocr |
| - receipts |
| - document-ai |
| - key-information-extraction |
| - post-ocr-parsing |
| source_datasets: |
| - naver-clova-ix/cord-v1 |
| --- |
| |
| # CORD (receipts) — reshaped mirror |
|
|
| A reshaped mirror of [naver-clova-ix/cord-v1](https://huggingface.co/datasets/naver-clova-ix/cord-v1) (Park et al., NAVER Clova, 2019), packaged for one-row-per-receipt ingestion. Upstream stores the receipt as a HuggingFace Image feature (a `{bytes, path}` struct) plus a `ground_truth` JSON string; pipelines that can't decode a struct image column (or don't want a full `datasets` dependency) can't consume it directly. This mirror splits each receipt into an image file + a JSONL annotation line, joinable by filename. |
|
|
| Re-hosted under Heliosoph for ingestion-pipeline stability. The annotations are CORD's, unchanged in content; the images are re-encoded (see below). Pin a revision for reproducible fetches. |
|
|
| Credit: Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, Hwalsuk Lee (CORD); NAVER Clova. |
|
|
| ## Why a mirror? |
|
|
| 1. **Struct image column.** Upstream's `image` is a HuggingFace Image feature — a `{bytes, path}` struct in parquet. Readers without nested-type support can't pull the bytes out. |
| 2. **Heavy PNGs.** The embedded images are full-resolution PNG photos (~2 MB each, ~2 GB for the full set). This mirror re-encodes them to **JPEG q90** — visually lossless for OCR, ~6× smaller (~210 MB total). |
|
|
| The `ground_truth` annotations are copied verbatim (parsed from the upstream JSON string into a nested object). |
|
|
| ## What this repo contains |
|
|
| ``` |
| cord-train-images.zip # 800 receipt JPEGs (entry name == image_file) |
| cord-train.jsonl.gz # 800 lines, one JSON object per receipt |
| cord-validation-images.zip # 100 receipts |
| cord-validation.jsonl.gz # 100 lines |
| cord-test-images.zip # 100 receipts |
| cord-test.jsonl.gz # 100 lines |
| ``` |
|
|
| ### Annotation JSONL — one receipt per line |
|
|
| ```json |
| { |
| "image_file": "cord-test-0000.jpg", |
| "width": 432, |
| "height": 648, |
| "ground_truth": { |
| "gt_parse": { "menu": { "nm": "-TICKET CP", "cnt": "2", "price": "60.000" }, |
| "sub_total": { "subtotal_price": "60.000", "tax_price": "5.455" }, |
| "total": { "total_price": "60.000" } }, |
| "valid_line": [ { "words": [ { "text": "...", "quad": { "x1": 0, "y1": 0, ... } } ], |
| "category": "menu.nm" } ], |
| "meta": { "image_size": { "width": 432, "height": 648 } }, |
| "roi": { "x1": 0, "y1": 0, ... } |
| } |
| } |
| ``` |
|
|
| - `image_file` is the **exact** entry name inside the images zip — join on it directly, no filename parsing. |
| - **`valid_line`** is the word-level OCR ground truth: each word's `text`, quadrilateral box, and semantic `category`. |
| - **`gt_parse`** is the structured key-information-extraction target: menu lines, sub-totals, totals. |
|
|
| ## How to use |
|
|
| ```python |
| import gzip, json, zipfile, io |
| from PIL import Image |
| |
| with gzip.open("cord-test.jsonl.gz", "rt", encoding="utf-8") as f: |
| receipts = [json.loads(line) for line in f] |
| |
| with zipfile.ZipFile("cord-test-images.zip") as z: |
| r = receipts[0] |
| img = Image.open(io.BytesIO(z.read(r["image_file"]))) |
| print(img.size, r["ground_truth"]["gt_parse"]) |
| ``` |
|
|
| ## Dataset specs |
|
|
| | | Spec | |
| |---|---| |
| | Receipts | 800 train / 100 validation / 100 test | |
| | Row granularity | one receipt (image + width/height + full ground_truth) | |
| | OCR ground truth | word-level: text + quad box + category (`valid_line`) | |
| | Parse ground truth | structured menu / sub_total / total (`gt_parse`) | |
| | Images | JPEG q90 (re-encoded from upstream full-res PNG) | |
| | Domain | Indonesian restaurant receipts (photographed) | |
| | Format | images as zip per split; annotations as gzipped JSONL | |
|
|
| ## When to pick CORD |
|
|
| - **Receipt OCR**: detection + recognition on real photographed receipts (skew, glare, thermal paper). |
| - **Key-information extraction / post-OCR parsing**: turn a receipt image into structured menu/total fields — the task CORD was built for. |
| - **Document-AI prototyping**: word text + box + category is the shape LayoutLM / Donut-style models consume. |
|
|
| For dense printed pages use **DocBank**; for scene text in the wild, **TextOCR** / **HierText**. |
|
|
| ## License |
|
|
| **CC BY 4.0**, as released for CORD. Permits commercial use, modification, and redistribution with attribution to NAVER Clova and the CORD authors. |
|
|
| - Paper: [CORD: A Consolidated Receipt Dataset for Post-OCR Parsing](https://openreview.net/forum?id=SJl3z659UH) |
| - Upstream: [naver-clova-ix/cord-v1](https://huggingface.co/datasets/naver-clova-ix/cord-v1) |
|
|
