README.md

---
license: mit
task_categories:
  - image-to-text
language:
  - en
tags:
  - ocr
  - document-understanding
  - table-extraction
  - structured-extraction
size_categories:
  - n<1K
---

# OCR Benchmark — Documents

The 93 document images and ground truth used by the
[ocr-benchmark](https://github.com/ilsilfverskiold/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](https://huggingface.co/datasets/jsdnrs/ICDAR2019-SROIE) (Huang et al.) | 6 | CC-BY-4.0 | Word annotations (human) |
| [invoices-and-receipts_ocr_v1](https://huggingface.co/datasets/mychen76/invoices-and-receipts_ocr_v1) | 6 | None declared | `parsed_data` field values (human-labeled) |
| [OmniAI OCR Benchmark](https://huggingface.co/datasets/getomni-ai/ocr-benchmark) | 40 | MIT | `true_markdown_output` (human-annotated) |
| [FUNSD](https://huggingface.co/datasets/nielsr/funsd) (Jaume et al. 2019) | 6 | Non-commercial research/educational use | Word annotations (human) |
| [IAM Handwriting](https://huggingface.co/datasets/Teklia/IAM-line) (FKI/Univ. Bern) | 6 | Registration-required, non-commercial research | Line transcriptions (human); 15 lines collaged per page |
| [IDL-WDS](https://huggingface.co/datasets/pixparse/idl-wds) (UCSF Industry Documents) | 6 | Custom "idl-train" license | Legacy OCR annotations (machine-generated) |
| [SynFinTabs](https://huggingface.co/datasets/ethanbradley/synfintabs) | 5 | MIT | Exact synthetic cell text |
| [RVL-CDIP](https://huggingface.co/datasets/nielsr/rvl_cdip_10_examples_per_class) (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](https://github.com/ilsilfverskiold/ocr-benchmark) under
`documents/` — and every benchmark run prints them before any score.

## Usage

Load directly:

```python
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:

```bash
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.