| --- |
| 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. |
|
|
