SymageDocsSymageDocs

Synthetic Document Data
From People Who Don't Exist

Synthetic documents and coherent identity data for training document AI, OCR, and NLP models with cross-field dependencies and record-level consistency that random generators can't reproduce.

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Why Synthetic Data?

Access to real-world data is constrained by privacy regulations and re-identification risk. SymageDocs generates statistically grounded synthetic populations with preserved cross-field dependencies and internally consistent identities — across forms like W-2s, 1040s, and CMS-1500 healthcare claims — all without using any real personal data.

Train document AI, OCR, parsing, and NLP systems on structurally realistic data while removing compliance and privacy exposure from the pipeline.

Data Quality

Coherent Identities, Not Random Fields

Not random rows. Coherent identity records that behave like real people.

Most synthetic data tools generate independent records. SymageDocs creates complete identities where every attribute logically aligns.

Typical Synthetic Data

NameAgeOccupationAddressStatus
Michael Chen19unrealisticCardiologistmismatchMiami, FLMarried
Susan Alvarez82unrealisticCollege StudentmismatchAustin, TXSingle

Randomly generated records. Fields are independent and often statistically unrealistic — a 19-year-old cardiologist, an 82-year-old college student.

Symage Synthetic Identity

MC

Michael Chen

Coherent Identity

NameMichael Chen
Age45
OccupationCardiologist
AddressBoston, MA
Marital StatusMarried
SpouseEmily Chen
Children2
Driver LicenseMassachusetts
PassportUScross-doc
Age-career matchState consistencyHousehold coherentCross-doc ready

One internally consistent identity. Age, occupation, household, and documents all reflect real-world statistical structure and cross-field dependencies.

Most tools generate rows. SymageDocs creates structured data with a story.

How Our Synthetic Document Generation Works

A simple three-step process: create the synthetic identity, populate the form, download your dataset.

1
Generate the Person
2
Populate the Form
3
Download Your Dataset
Synthetic Identity
MC

Maria Chen

Age 34 · Software Engineer

NameMaria Chen
SSN***-**-7294
DOBJun 08, 1990
AddressPortland, OR
EmployerNexus Corp
Salary$92,400/yr
Cross-field dependencies preserved
W-2 Form
42 fields
Employee nameMaria Chen
Wages, tips$92,400.00
Federal tax$15,872.00
StateOR
Employer EIN47-***3861
Filing statusSingle
W-210991040CMS-1500
Your Dataset
CSV
JSON
PDF
Labels
---
identities per job
Pipeline readyGround truth labels

Running Automated Training Pipelines? Use our SDK.

The SymageDocs Python SDK lets you request generation, download your data, and feed it directly into your training pipeline — all from Python. No browser, no zip files, no manual steps. Available on PyPI.

pip install symagedocs

Why ML Teams Choose SymageDocs for Document Training Data

Coherent Identities, Not Random Fields.

Each synthetic identity preserves cross-field dependencies and real-world correlations. Income distributions, filing patterns, and demographic attributes reflect real-world statistical structure.

We Take the P out of PII.

Our data is programmatically generated, not de-identified or transformed from real individuals. No underlying PII and no re-identification risk. Use it confidently across teams, regions, and regulatory frameworks.

Growing Library of Documents. Pipeline Ready.

Tax returns, healthcare claims, insurance applications, legal documents spanning multiple form types and versions. Every document preserves cross-document identity consistency.

Pipeline-Ready Export Formats.

Download labeled training data in FUNSD, YOLOv8, COCO JSON, and BIO token classification formats — the formats your pipeline already expects. No conversion scripts, no post-processing. Ground-truth labels are included automatically.

Built for the World's Most Regulated Industries

HIPAA-Ready

AI training without patient data risk.

GDPR-Safe

No personal data. No privacy exposure.

SOC 2 Aligned

Built to meet enterprise security standards.

PCI-DSS Friendly

Train models without exposing financial records.

Synthetic Document Data Use Cases

OCR & Document Parsing

Generate thousands of filled forms — handwritten, typed, scanned — to train document extraction models without touching a single real tax return or medical record.

Fraud Detection Systems

Train models to detect altered, forged, or inconsistent forms using both clean and intentionally corrupted synthetic datasets generated from the same underlying population model.

KYC & Onboarding Workflows

Test identity verification pipelines using synthetic applicants whose IDs, addresses, and supporting documents all corroborate because they're generated from the same underlying identity record.

AI Pipeline Dev & QA

Replace production data in dev and staging environments with SymageDocs output. Your engineers get realistic data. Your compliance team sleeps soundly.

Simple Pricing

Start free. Scale when you need more.

Free

$0

Try it out

  • 250 credits/month
  • PDF + JSON + CSV output
  • Preview up to 3 identities
Get Started
Most Popular

Pro

$79/mo

For growing teams

  • 1,600 credits/month
  • Credit packs from $0.05/credit
  • PDF + JSON + CSV output
  • Handwritten output
  • Priority support
Upgrade

Scale

$175/mo

For production workloads

  • 5,000 credits/month
  • Credit packs from $0.05/credit
  • PDF + JSON + CSV output
  • Custom forms
  • Handwritten output
  • Priority support
Upgrade

Enterprise

Custom

Custom volume & SLA

  • Unlimited credits
  • PDF + JSON + CSV output
  • Custom forms
  • API access
  • Deploy behind your own firewall
  • Dedicated support
  • SLA

How credits work

  • Simple typed PDFs cost 20 credits (~$1 at base rate)
  • Handwritten PDFs cost 40 credits (~$2 at base rate)
  • Complex forms (25+ fields) may use additional credits based on complexity
  • Tabular datasets: 50 rows per credit

Frequently Asked Questions

What is synthetic document data?+
Synthetic document data consists of realistic but entirely fictional records such as tax returns, healthcare claims, insurance applications, generated from scratch with no underlying real-world source. There is no PII and no re-identification risk, making it safe to use for ML training, system testing, and compliance validation across any regulatory framework.
Is SymageDocs data HIPAA and GDPR compliant?+
Yes. Because SymageDocs generates data from simulated identities rather than transforming real records, there are no data subjects and no protected health information involved. This means HIPAA, GDPR, and CCPA obligations do not apply to SymageDocs output. You can share it freely across teams, regions, and environments without compliance review.
What forms does SymageDocs support?+
SymageDocs supports a growing library of U.S. government, healthcare, and tax forms including W-2s, 1099s, 1040s, CMS-1500 healthcare claims, insurance applications, and more. Each form is filled with data from coherent synthetic identities, so a W-2 and a 1040 from the same person will be internally consistent.
How is SymageDocs different from Faker or random data generators?+
Faker and similar libraries generate random, independent field values — a random name, a random address, a random SSN with no relationship between them. SymageDocs simulates complete life histories: occupation determines income, income determines tax brackets, address determines state filing requirements, and household composition determines dependents. The result is structurally realistic data that trains better models.
Can I use SymageDocs to train Google Document AI or Azure AI Document Intelligence?+
Absolutely. SymageDocs output includes filled PDFs with pixel-perfect bounding box annotations in the formats these platforms require. Google Document AI's foundation model can fine-tune on as few as 5 labeled documents per form type — SymageDocs can generate thousands in minutes, with ground-truth labels included automatically.
What output formats are available?+

SymageDocs generates filled PDF documents in both typed and handwritten styles, structured JSON with all identity and field data, and CSV exports. For ML training, every document includes bounding box annotations with coordinate data and key-value pair labels for every filled field.

Labeled exports are available in FUNSD, YOLOv8, COCO JSON, and BIO token classification formats, covering the most common pipelines for document extraction, object detection, and NER tasks.

If we don't support the format you're looking for, use the Feedback button in the bottom right of the screen to let us know what format you'd like to see. We may add it to our roadmap.

How do I request a custom form?+
If your form isn’t in the current library, you can submit a request directly from within the platform. Once submitted, you’ll receive an email to let you know if it’s a form we’ll be able to add. These typically take between a few business days and a week, and you’ll receive email updates throughout the process.

Start With 250 Credits. Free.

No credit card. No commitment. Generate your first synthetic dataset from SymageDocs in under two minutes.

Generate Your First Dataset