README.md

---
pretty_name: SymageDocs — Synthetic US Forms Preview (FUNSD / LayoutLM)
license: cc-by-4.0
language:
- en
task_categories:
- token-classification
- object-detection
- image-to-text
- visual-document-retrieval
annotations_creators:
- machine-generated
language_creators:
- machine-generated
source_datasets:
- original
tags:
- document-ai
- document-understanding
- funsd
- layoutlm
- layoutlmv3
- donut
- ocr
- forms
- synthetic
- synthetic-data
- key-information-extraction
- document-question-answering
- coco
- yolo
- invoice
- tax-forms
- us-forms
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# SymageDocs — Synthetic US Forms Preview

A small, **CC-BY-4.0**, fully synthetic document-AI training set: **525
labeled page images** across **six families of US business and government forms**,
each page shipping FUNSD ground truth plus a LayoutLM-ready token/bbox/tag view.

This is a **preview subset**. It exists so you can load real output from the
[SymageDocs](https://symagedocs.ai) generator, inspect the label quality, and
decide whether generating your own corpus is worth your time — without an
account, an email, or a click-through.

- **525** page-level rows (train 420 / test 105)
- **10** form templates across **6** families
- Clean typed renders at **300 DPI**
- **CC-BY-4.0** — use it commercially, redistribute it, remix it. Just credit SymageDocs.

## What's in it

| form_id | Form | Documents | Pages each |
|---|---|---|---|
| `irs_w2_single_page_2026` | IRS Form W-2 (single page, 2026) | 150 | 1 |
| `irs_f1040_modern_2024` | IRS Form 1040 (2024) | 50 | 2 |
| `cms_1500_standard_02_12` | CMS-1500 health insurance claim (02/12) | 50 | 1 |
| `irs_w9_standard_2024` | IRS Form W-9 (2024) | 50 | 1 |
| `i9_standard_2024` | USCIS Form I-9 (2024) | 25 | up to 3 |
| `invoice_classic` … `invoice_construction` | Commercial invoices, 5 layouts | 20 each | 1 |

Form **layouts** are US-government public domain (IRS, USCIS) or NUCC public
domain (CMS-1500); the invoice layouts are original to SymageDocs. Medical
procedure codes are HCPCS Level II or synthetic — no licensed AMA CPT
descriptor text appears anywhere in this data.

## Schema

| Column | Type | Description |
|---|---|---|
| `image` | image | Clean typed page render, 300 DPI PNG |
| `tokens` | list[str] | Word tokens in FUNSD reading order |
| `bboxes` | list[[x0,y0,x1,y1]] | Per-token boxes, normalized 0–1000 (LayoutLM convention) |
| `ner_tags` | ClassLabel seq | BIO tags: `O, B-HEADER, I-HEADER, B-QUESTION, I-QUESTION, B-ANSWER, I-ANSWER` |
| `funsd_json` | str | Full FUNSD `form` array — entities, boxes, **key–value linking**, checkbox states |
| `coco_json` | str | COCO-style **field-region** detection annotations for the page — one box per form field |
| `yolo_txt` | str | YOLO-format **per-word** boxes for the page — one line per rendered word, classed by structural role |
| `form_id` | str | Which form this page belongs to |
| `identity_id` | int | Document key — groups a multi-page form's pages |
| `page` | int | Page index within the document |
| `num_entities` | int | Entity count (see the note on unfilled supplement pages) |

Four label views ship for every page — **FUNSD**, **BIO token tags**, **COCO**,
and **YOLO** — so you can benchmark token classification and field-region
detection off the same images without re-annotating.

**`coco_json` and `yolo_txt` are different granularities, on purpose.** COCO
boxes are *field regions* (one per form field); YOLO boxes are *words* (one per
rendered word). They are complementary views, **not** the same annotation in two
file formats — do not train a detector on one and evaluate it against the other.
For field-region detection use `coco_json`; for word-level detection or reading
order use `yolo_txt` (or `bboxes`, which is the same granularity in LayoutLM
convention).

`yolo_txt` class ids are a fixed 6-class structural vocabulary, constant across
every SymageDocs dataset and aligned with the FUNSD taxonomy:

```
0 question
1 answer
2 header
3 label
4 instruction
5 other
```

Splits are drawn at the **document** level, so a multi-page form never straddles
train and test.

### Unfilled supplement pages are intentional

I-9 Supplement A (Preparer/Translator) and Supplement B (Reverification/Rehire)
are optional pages most employees never trigger, so they render with **no values
filled in** — exactly as they sit in a real HR file. Those rows still carry the
page image and the supplement's labeled-but-empty field regions (`funsd_json`,
`coco_json` and `yolo_txt` are all populated; `num_entities` is ~12) but **zero
tokens**. A model that reads real I-9s must also recognize an unfilled
supplement.

For a pure token-classification subset, filter on **`len(tokens) > 0`**.
`num_entities > 0` does **not** exclude these rows.

## Load it

```python
from datasets import load_dataset

ds = load_dataset("Symage/synthetic-us-forms-preview")
row = ds["train"][0]
print(row["form_id"], row["tokens"][:10], row["ner_tags"][:10])
row["image"].show()
```

The `tokens` / `bboxes` / `ner_tags` triple is already in the shape
`LayoutLMv3Processor` expects, so the standard `funsd-layoutlmv3` fine-tuning
recipe runs against this set unchanged.

## How it was generated

Every record is produced by the SymageDocs generation engine: a synthetic
identity is sampled, propagated through a form's field bindings and computed
fields, rendered to PDF, and rasterized — with the word-level annotations
captured from the renderer itself rather than recovered by OCR. **The boxes are
exact by construction, not estimated**, and no OCR error is baked into the
ground truth.

Generation runs through the same production code path that serves paying
customers, from a pinned seed, so this subset is reproducible rather than a
hand-curated showcase.

### Zero PII by construction

There is no real personal data here, because there was never any real personal
data in the pipeline: names, addresses, SSNs, EINs, NPIs, and account numbers
are all generated from synthetic distributions and validated check-digit
schemes. Nothing was scraped, de-identified, or anonymized from real records —
so there is no re-identification risk to reason about.

Because values are generated programmatically, a record may coincidentally
resemble a real person's details. It is still synthetic, and it is not derived
from anyone's records.

## Honest limitations

Worth knowing before you build on it:

- **Clean renders only.** No scanner noise, skew, blur, or ink bleed in this
  subset. Models trained on it alone will not be robust to photographed or
  faxed documents. The generator produces graded degradation profiles; this
  preview deliberately ships the clean tier.
- **Small.** It is a preview, sized to be inspected, not to saturate a model.
- **Independent documents.** Each document has its own identity. Cross-document
  coherence is a separate dataset (below).
- **US forms, English only.**
- **No published benchmark.** We have not trained a reference model on this
  subset, so we quote no accuracy numbers. Judge it by inspecting the labels.

## Related: the full coherent dataset

[`Symage/coherent-forms-1040-cms1500-i9`](https://huggingface.co/datasets/Symage/coherent-forms-1040-cms1500-i9)
is a larger set where **one synthetic person's** IRS 1040, CMS-1500, and I-9 are
all filled from the **same** identity — name, SSN, address, and employer flow
consistently across all three documents, which is much closer to a real
onboarding or claims packet than a bag of unrelated pages. It is free, behind a
one-click license acceptance, under a different (non-CC-BY) license.

Coherent multi-form packages are the thing the generator does that a static
dataset can't hand you; this preview is the single-document taste of it.

## Generate your own

This subset is a fixed slice. The engine behind it takes parameters:

- **50+ form types** — tax, healthcare, insurance, HR and onboarding,
  financial, commercial. Browse the catalog at
  [symagedocs.ai/forms](https://symagedocs.ai/forms).
- **Label formats** — FUNSD, BIO, COCO, YOLO, Donut image→JSON, plus raw
  per-field ground truth as JSON and CSV.
- **Renders** — typed and handwritten PDF, pre-filled PDF, PNG at your DPI.
- **Degradation** — clean through heavily scanned, graded intensity: skew,
  blur, JPEG artifacts, ink bleed, stains.
- **Coherent multi-form identity packages** at whatever volume you need.

```bash
pip install symagedocs
```

The free tier starts you with **1,000 credits** (a promotional 500/month
allowance plus a 500-credit welcome bonus) — enough to generate a real corpus
and check it against your pipeline before deciding anything.
→ [symagedocs.ai](https://symagedocs.ai)

## License

**CC-BY-4.0.** You may use, modify, and redistribute this subset, including
commercially and including for model training. Please credit SymageDocs and
link back to [symagedocs.ai](https://symagedocs.ai).

Note that the related coherent dataset linked above is under different, more
restrictive terms — this CC-BY-4.0 grant covers **this** preview subset only.

## Citation

```bibtex
@misc{symagedocs_us_forms_preview,
  title  = {SymageDocs — Synthetic US Forms Preview (FUNSD / LayoutLM)},
  author = {Symage, Inc.},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Symage/synthetic-us-forms-preview}
}
```