hyperbots-inc/synthetic-doc-splitting

Synthetic Document Splitting

200 multi-document bundles, the kind of mixed packet that lands in an AP, claims or onboarding inbox. Each bundle holds 2 to 7 documents of any of 31 enterprise types and runs to several pages. Some have fax cover sheets, continuation pages, "intentionally left blank" pages and back-to-back documents of the same type. The task is to find where each document starts and ends.

It matches HyperAPI POST /v1/split, which returns split_label, start_page and end_page for each part.

What makes it hard

• Same-type neighbours: about a quarter of documents are followed by another of the same type, such as two invoices back to back, so the type alone doesn't reveal a boundary.
• Multi-page documents: long statements and tables run across pages. Continuation pages sometimes repeat the title ("... - continued") and sometimes don't.
• Page numbers aren't reliable: only about 60% of pages carry "Page i of n".
• Separators: cover sheets (labelled cover_sheet, taxonomy 100) and blank pages attached to the preceding document.

Columns

Column	Description
id	Bundle id
num_pages, num_documents	Bundle size
pages	List of {page_number, text, html}
segments	List of {split_label, taxonomy_id, taxonomy_name, title, start_page, end_page} (1-based, inclusive)

from datasets import load_dataset
ds = load_dataset("hyperbots-inc/synthetic-doc-splitting", split="test")
b = ds[0]
print(b["num_pages"], [(s["split_label"], s["start_page"], s["end_page"]) for s in b["segments"]])

To test HyperAPI Split, render each page's html into one multi-page PDF (one page per entry).

Suggested metrics

Use boundary precision, recall and F1, where a boundary is a page that starts a new document; exact segment match; and segment-label accuracy.

How the data was made

Every document is generated from a seeded random number generator by docgen.js and rendered on an HTML canvas in Chrome on macOS. Images use the system's print fonts and, for handwriting, the handwriting-style fonts installed with macOS (Bradley Hand, Noteworthy, Marker Felt, Chalkboard SE, Snell Roundhand, Apple Chancery, Savoye LET, Brush Script MT). Labels are taken from the generator itself, so there is no human annotation error. Field and block boxes are recorded at the moment each element is drawn.

All companies, people, addresses, account numbers and amounts are fictional. Phone numbers use ranges reserved for fiction where these exist (US 555-01xx, UK 07700 900xxx, AU 0491 570 1xx). Email addresses use the reserved example.com / example.org domains, and US ID numbers start with 9, which is never issued as an SSN.

To rebuild the dataset, load generator/docgen.js and generator/datasets.js in a browser page and run await DocSets.build('splitting'). Rendering depends on installed fonts, so pixels can differ on other systems, but text and labels are identical for the same seed.

Limitations

• The documents are synthetic. Their layouts, vocabulary and value ranges are much narrower than real enterprise traffic. Use them for benchmarking, regression tests and pipeline development, not as a replacement for evaluation on your own documents.
• All content is in English, with values formatted for 9 locales (US, UK, IN, CA, AU, DE, FR, SG, AE).
• Handwriting is rendered from fonts with per-character jitter. It is a stand-in for real handwriting, not a sample of it.

About HyperAPI

HyperAPI is Hyperbots' document intelligence API for enterprise documents. It provides parse, extract, classify, split and redact behind one endpoint and returns schema-validated JSON with a confidence score on every field. It has a REST API and a Python SDK, with an MCP server for agents available on request, and you can start free. See the HyperAPI Space for a walkthrough.

Part of the Hyperbots synthetic document suite

Dataset	Focus
synthetic-enterprise-doc-extraction	Field extraction across 31 enterprise document types
synthetic-doc-parsing	Layout blocks, reading order, Markdown and tables
synthetic-pii-redaction	PII spans and boxes for masking and de-identification
synthetic-doc-classification	31-class document classification
synthetic-doc-splitting (this dataset)	Multi-document bundles with true boundaries
synthetic-handwritten-forms	Forms filled in handwriting-style script
synthetic-stamped-documents	Stamps and seals over printed text
synthetic-bad-scans	Skew, blur, noise, shadows and compression at 3 severities
synthetic-dense-documents	Small type, multi-column layouts and long tables
synthetic-invoice-extraction	Invoice fields and line items (text + HTML)

License and citation

CC BY 4.0. Generator code is MIT.

@misc{hyperbots2026docsplitting,
  title  = {Synthetic Document Splitting},
  author = {Hyperbots Inc.},
  year   = {2026},
  url    = {https://huggingface.co/datasets/hyperbots-inc/synthetic-doc-splitting}
}