ilsilfverskiold/ocr-benchmark

OCR Benchmark — Documents

The 93 document images and ground truth used by the ocr-benchmark harness. The benchmark code, the reference run results, and the full methodology live in the GitHub repo — this dataset is the document corpus only.

Structure

One train split, 93 rows, one row per document:

Column	Type	Description
image	Image	The document page (PNG/JPG)
stem	string	Filename stem (e.g. invoice_000)
tier	string	Difficulty: easy, medium, or hard
doc_type	string	Document type (e.g. invoice, receipt, tax_form)
gt_text	string	Ground-truth plain text (null for 11 silver-GT docs)
gt_json	string	Serialized JSON — plain_text and table_html fields
structured_gt	string	Serialized JSON — json_schema and true_json (schema varies per doc type)
markdown_gt	string	Ground-truth markdown (null if unavailable)
geometry_gt	string	Bounding-box ground truth as serialized JSON (null if unavailable)

JSON columns are stored as strings because their internal structure varies per document type (a tax form's schema is not a receipt's schema). Parse with json.loads().

Document types

Tier	Types (count each)	Total
easy	receipt (6), invoice (6)	12
medium	bank_statement, shipping, tax_form, medical, payslip, lease, photo_receipt (5 each)	35
hard	form (6), handwritten (6), scanned_legacy (6), financial_table (5), chart (5), newspaper (6), legal (6), report (6)	46

Where the documents come from

Every image is an excerpt from a public dataset. Documents were selected deterministically (fixed row indices), not cherry-picked by content.

Source	Docs	License	GT provenance
ICDAR2019-SROIE (Huang et al.)	6	CC-BY-4.0	Word annotations (human)
invoices-and-receipts_ocr_v1	6	None declared	parsed_data field values (human-labeled)
OmniAI OCR Benchmark	40	MIT	true_markdown_output (human-annotated)
FUNSD (Jaume et al. 2019)	6	Non-commercial research/educational use	Word annotations (human)
IAM Handwriting (FKI/Univ. Bern)	6	Registration-required, non-commercial research	Line transcriptions (human); 15 lines collaged per page
IDL-WDS (UCSF Industry Documents)	6	Custom "idl-train" license	Legacy OCR annotations (machine-generated)
SynFinTabs	5	MIT	Exact synthetic cell text
RVL-CDIP (Harley et al.)	18	Research use	Silver — Tesseract at build time; judge-only evaluation

FUNSD, IAM, and RVL-CDIP (30 of 93 docs) carry research-use or non-commercial restrictions. This dataset redistributes small excerpts solely for reproducible research benchmarking, with full attribution. If you are a rights holder and want a document removed, open an issue — it will be removed promptly.

Ground-truth provenance

• Human (75 docs) — source dataset annotations
• Model-verified (8 docs) — model-transcribed, cross-checked against 14-engine consensus
• Silver (11 docs) — machine-generated (Tesseract); scored by LLM judge only, never by text metrics

Per-document provenance, known limitations, and the full audit trail are in the GitHub repo under documents/ — and every benchmark run prints them before any score.

Usage

Load directly:

from datasets import load_dataset
import json

ds = load_dataset("ilsilfverskiold/ocr-benchmark", split="train")
row = ds[0]
row["image"]                          # PIL image
gt = json.loads(row["structured_gt"]) # json_schema + true_json

Or use it through the benchmark harness, which rebuilds the on-disk corpus layout and can rerun any engine against these documents:

git clone https://github.com/ilsilfverskiold/ocr-benchmark
cd ocr-benchmark
pip install -r requirements.txt
python scripts/download_data.py     # fetches this dataset
python run_benchmark.py --engines tesseract,docling --tiers easy --skip-judge

The reference benchmark results (14 engines × 93 documents × 7 legs) ship with the GitHub repo — you only need this dataset if you want to rerun engines.

License

The images are excerpts from third-party datasets and keep their original licenses — see the source table above. The permissive subset (MIT / CC-BY-4.0) covers 57 of the 93 documents.