--- 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} } ```