Datasets:

hyperbots-inc
/

synthetic-doc-classification

---
license: cc-by-4.0
language:
  - en
pretty_name: Synthetic Enterprise Document Classification (31 classes)
size_categories:
  - n<1K
task_categories:
  - image-classification
  - text-classification
tags:
  - synthetic
  - document-ai
  - enterprise-documents
  - hyperapi
  - document-classification
  - document-routing
  - taxonomy
---
# Synthetic Enterprise Document Classification

**248 page images across 31 enterprise document classes (8 per class)**. Labels come from Hyperbots' 100-type reference taxonomy and cover finance, procurement, logistics, HR, onboarding and compliance, healthcare, legal and facilities documents. About 20% of pages are stamped and about 20% are degraded scans, so the classifier can't rely on clean pixels alone.

It matches [HyperAPI](https://apis.hyperbots.com) `POST /v1/classify`, which returns a label, a confidence score and ranked candidates, and supports custom classes.

## Classes

| ID | Type | Category | `doc_type` |
|---|---|---|---|
| 1 | Invoice | A. Payables & Invoices | `invoice` |
| 10 | Tax Invoice | A. Payables & Invoices | `tax_invoice` |
| 11 | Statement of Account / Aging Statement | B. Account Statements & Reconciliation | `statement_of_account` |
| 13 | Credit Card Statement | B. Account Statements & Reconciliation | `credit_card_statement` |
| 14 | Bank Statement | B. Account Statements & Reconciliation | `bank_statement` |
| 15 | Credit Note | C. Credits & Adjustments | `credit_note` |
| 17 | Receipt | D. Payments & Settlements | `receipt` |
| 20 | Remittance Advice | D. Payments & Settlements | `remittance_advice` |
| 25 | Dunning / Collection Letter | E. Collections | `dunning_letter` |
| 28 | Purchase Order | F. Procurement & Ordering | `purchase_order` |
| 30 | Quotation / Estimate | F. Procurement & Ordering | `quotation` |
| 32 | Work Order | F. Procurement & Ordering | `work_order` |
| 38 | Delivery Note / Challan | G. Shipping & Logistics | `delivery_note` |
| 39 | Bill of Lading | G. Shipping & Logistics | `bill_of_lading` |
| 41 | Packing List | G. Shipping & Logistics | `packing_list` |
| 42 | Timesheet | G. Shipping & Logistics | `timesheet` |
| 43 | Expense Report | H. Internal & Employee Documents | `expense_report` |
| 44 | Payroll Statement | H. Internal & Employee Documents | `payroll_statement` |
| 51 | Bank Details Form | I. Onboarding & Compliance | `bank_details_form` |
| 54 | KYC Documentation | I. Onboarding & Compliance | `kyc_form` |
| 56 | Balance Sheet | J. Financial Statements & Reports | `balance_sheet` |
| 57 | Profit & Loss Statement | J. Financial Statements & Reports | `profit_and_loss` |
| 62 | Credit Application Form | K. Credit, Lending & Risk | `credit_application` |
| 79 | Lab Test Report / Result | L. Clinical, Laboratory & Healthcare | `lab_report` |
| 80 | Patient Intake / Registration Form | L. Clinical, Laboratory & Healthcare | `patient_intake_form` |
| 83 | Explanation of Benefits (EOB) / Claim Form | L. Clinical, Laboratory & Healthcare | `claim_form` |
| 87 | Meeting Minutes / Notes | M. Digital & Communication Artifacts | `meeting_minutes` |
| 90 | Non-Disclosure Agreement (NDA) | N. Legal, HR & Administrative | `nda` |
| 95 | Offer Letter / Employment Contract | N. Legal, HR & Administrative | `offer_letter` |
| 98 | Incident / Service & Maintenance Report | O. Operations, Quality & Facilities | `maintenance_report` |
| 99 | Lease Agreement / Utility Bill | O. Operations, Quality & Facilities | `utility_bill` |

Some pairs are deliberately hard: `invoice` vs `tax_invoice`, `bank_statement` vs `credit_card_statement` vs `statement_of_account`, `purchase_order` vs `quotation` vs `work_order`, and `delivery_note` vs `packing_list`. Titles vary within each class ("Credit Note" / "Credit Memo", "Payslip" / "Earnings Statement" / "Pay Advice").

## Columns

| Column | Description |
|---|---|
| `image` | 744 x 1052 JPEG (A4 at about 90 DPI) |
| `label` | Class key (`doc_type`) |
| `taxonomy_id`, `taxonomy_name`, `category`, `financial` | Taxonomy metadata |
| `title_on_page` | The printed title (don't feed it to the model if you want a fair test) |
| `variant` | `clean`, `stamped`, `degraded` or `stamped+degraded` |
| `ocr_text` | Page text, for text-only classifiers |

```python
from datasets import load_dataset
ds = load_dataset("hyperbots-inc/synthetic-doc-classification", split="test")
print(ds.unique("label")[:5], ds[0]["variant"])
```

## How the data was made

Every document is generated from a seeded random number generator by [`docgen.js`](https://huggingface.co/datasets/hyperbots-inc/synthetic-enterprise-doc-extraction/tree/main/generator) 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('classification')`. 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](https://apis.hyperbots.com) 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](https://apis.hyperbots.com/dashboard/register). See the [HyperAPI Space](https://huggingface.co/spaces/hyperbots-inc/HyperAPI) for a walkthrough.

## Part of the Hyperbots synthetic document suite

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

## License and citation

CC BY 4.0. Generator code is MIT.

```bibtex
@misc{hyperbots2026docclassification,
  title  = {Synthetic Enterprise Document Classification},
  author = {Hyperbots Inc.},
  year   = {2026},
  url    = {https://huggingface.co/datasets/hyperbots-inc/synthetic-doc-classification}
}
```

	---
	license: cc-by-4.0
	language:
	- en
	pretty_name: Synthetic Enterprise Document Classification (31 classes)
	size_categories:
	- n<1K
	task_categories:
	- image-classification
	- text-classification
	tags:
	- synthetic
	- document-ai
	- enterprise-documents
	- hyperapi
	- document-classification
	- document-routing
	- taxonomy
	---

	# Synthetic Enterprise Document Classification

	248 page images across 31 enterprise document classes (8 per class). Labels come from Hyperbots' 100-type reference taxonomy and cover finance, procurement, logistics, HR, onboarding and compliance, healthcare, legal and facilities documents. About 20% of pages are stamped and about 20% are degraded scans, so the classifier can't rely on clean pixels alone.

	It matches [HyperAPI](https://apis.hyperbots.com) `POST /v1/classify`, which returns a label, a confidence score and ranked candidates, and supports custom classes.

	## Classes

	\| ID \| Type \| Category \| `doc_type` \|
	\|---\|---\|---\|---\|
	\| 1 \| Invoice \| A. Payables & Invoices \| `invoice` \|
	\| 10 \| Tax Invoice \| A. Payables & Invoices \| `tax_invoice` \|
	\| 11 \| Statement of Account / Aging Statement \| B. Account Statements & Reconciliation \| `statement_of_account` \|
	\| 13 \| Credit Card Statement \| B. Account Statements & Reconciliation \| `credit_card_statement` \|
	\| 14 \| Bank Statement \| B. Account Statements & Reconciliation \| `bank_statement` \|
	\| 15 \| Credit Note \| C. Credits & Adjustments \| `credit_note` \|
	\| 17 \| Receipt \| D. Payments & Settlements \| `receipt` \|
	\| 20 \| Remittance Advice \| D. Payments & Settlements \| `remittance_advice` \|
	\| 25 \| Dunning / Collection Letter \| E. Collections \| `dunning_letter` \|
	\| 28 \| Purchase Order \| F. Procurement & Ordering \| `purchase_order` \|
	\| 30 \| Quotation / Estimate \| F. Procurement & Ordering \| `quotation` \|
	\| 32 \| Work Order \| F. Procurement & Ordering \| `work_order` \|
	\| 38 \| Delivery Note / Challan \| G. Shipping & Logistics \| `delivery_note` \|
	\| 39 \| Bill of Lading \| G. Shipping & Logistics \| `bill_of_lading` \|
	\| 41 \| Packing List \| G. Shipping & Logistics \| `packing_list` \|
	\| 42 \| Timesheet \| G. Shipping & Logistics \| `timesheet` \|
	\| 43 \| Expense Report \| H. Internal & Employee Documents \| `expense_report` \|
	\| 44 \| Payroll Statement \| H. Internal & Employee Documents \| `payroll_statement` \|
	\| 51 \| Bank Details Form \| I. Onboarding & Compliance \| `bank_details_form` \|
	\| 54 \| KYC Documentation \| I. Onboarding & Compliance \| `kyc_form` \|
	\| 56 \| Balance Sheet \| J. Financial Statements & Reports \| `balance_sheet` \|
	\| 57 \| Profit & Loss Statement \| J. Financial Statements & Reports \| `profit_and_loss` \|
	\| 62 \| Credit Application Form \| K. Credit, Lending & Risk \| `credit_application` \|
	\| 79 \| Lab Test Report / Result \| L. Clinical, Laboratory & Healthcare \| `lab_report` \|
	\| 80 \| Patient Intake / Registration Form \| L. Clinical, Laboratory & Healthcare \| `patient_intake_form` \|
	\| 83 \| Explanation of Benefits (EOB) / Claim Form \| L. Clinical, Laboratory & Healthcare \| `claim_form` \|
	\| 87 \| Meeting Minutes / Notes \| M. Digital & Communication Artifacts \| `meeting_minutes` \|
	\| 90 \| Non-Disclosure Agreement (NDA) \| N. Legal, HR & Administrative \| `nda` \|
	\| 95 \| Offer Letter / Employment Contract \| N. Legal, HR & Administrative \| `offer_letter` \|
	\| 98 \| Incident / Service & Maintenance Report \| O. Operations, Quality & Facilities \| `maintenance_report` \|
	\| 99 \| Lease Agreement / Utility Bill \| O. Operations, Quality & Facilities \| `utility_bill` \|

	Some pairs are deliberately hard: `invoice` vs `tax_invoice`, `bank_statement` vs `credit_card_statement` vs `statement_of_account`, `purchase_order` vs `quotation` vs `work_order`, and `delivery_note` vs `packing_list`. Titles vary within each class ("Credit Note" / "Credit Memo", "Payslip" / "Earnings Statement" / "Pay Advice").

	## Columns

	\| Column \| Description \|
	\|---\|---\|
	\| `image` \| 744 x 1052 JPEG (A4 at about 90 DPI) \|
	\| `label` \| Class key (`doc_type`) \|
	\| `taxonomy_id`, `taxonomy_name`, `category`, `financial` \| Taxonomy metadata \|
	\| `title_on_page` \| The printed title (don't feed it to the model if you want a fair test) \|
	\| `variant` \| `clean`, `stamped`, `degraded` or `stamped+degraded` \|
	\| `ocr_text` \| Page text, for text-only classifiers \|

	```python
	from datasets import load_dataset
	ds = load_dataset("hyperbots-inc/synthetic-doc-classification", split="test")
	print(ds.unique("label")[:5], ds[0]["variant"])
	```

	## How the data was made

	Every document is generated from a seeded random number generator by [`docgen.js`](https://huggingface.co/datasets/hyperbots-inc/synthetic-enterprise-doc-extraction/tree/main/generator) 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('classification')`. 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](https://apis.hyperbots.com) 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](https://apis.hyperbots.com/dashboard/register). See the [HyperAPI Space](https://huggingface.co/spaces/hyperbots-inc/HyperAPI) for a walkthrough.

	## Part of the Hyperbots synthetic document suite

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

	## License and citation

	CC BY 4.0. Generator code is MIT.

	```bibtex
	@misc{hyperbots2026docclassification,
	title = {Synthetic Enterprise Document Classification},
	author = {Hyperbots Inc.},
	year = {2026},
	url = {https://huggingface.co/datasets/hyperbots-inc/synthetic-doc-classification}
	}
	```