Hugging Face's logo

Datasets:
Heliosoph
/
CORD

Tasks:
Image-to-Text
Object Detection
Languages:
English
Indonesian
Size:
n<1K
Tags:
cord
ocr
receipts
document-ai
key-information-extraction
post-ocr-parsing
License:
Dataset card Files
xet
 Community
CORD / README.md
flyingbertman's picture
flyingbertman
Upload folder using huggingface_hub
51b7e93 verified
preview code
|
Raw
History Blame Contribute Delete
4.82 kB
metadata 
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 (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 

{
  "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 

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.