# SymageDocs

> Generate realistic synthetic identities and filled government, healthcare, and tax forms for ML model training. No real PII. HIPAA-ready, GDPR-safe synthetic data at scale.

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.

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.

## Forms

Browse 59 synthetic document forms for ML training data. IRS tax forms, healthcare claims, and more — all with realistic synthetic data.

- [Synthetic Document Forms](https://symagedocs.ai/forms)
- [Invoice](https://symagedocs.ai/forms/invoice-classic): Generate synthetic commercial invoices in a traditional business layout with vendor/customer details, itemized line items, tax calculations, and payment terms. Ideal for training AP automation, OCR, and invoice processing pipelines on the most common invoice format.
- [Invoice](https://symagedocs.ai/forms/invoice-freelance): Generate synthetic freelance invoice training data with a warm, minimal layout of the kind independent contractors send through Wave, Square, and Bonsai. Sparse labels, a Rate column instead of Unit Price, and no PO number — the hardest invoice variant for extractors trained on dense enterprise templates.
- [Invoice](https://symagedocs.ai/forms/invoice-modern): Generate synthetic modern SaaS-style invoice training data — dark header accent, borderless line-item table, uppercase FROM / BILL TO blocks, PO number and discount lines. The billing-platform aesthetic (Stripe, FreshBooks, Bill.com) that most AP extractors see least often.
- [Invoice](https://symagedocs.ai/forms/invoice-service): Generate synthetic professional services invoice training data for consulting, legal, and accounting billing — a Qty/Hrs column, wide narrative descriptions, a PO reference, and a printed late-fee policy block. The time-and-materials invoice shape that legal-spend and services-AP models have to read.
- [CMS-1500 - Health Insurance Claim Form](https://symagedocs.ai/forms/cms-1500): Generate synthetic CMS-1500 health insurance claim forms with realistic patient demographics, diagnosis codes, and procedure data. The standard healthcare claim form used across the U.S. — essential training data for healthcare document AI and claims processing pipelines.
- [Form 1040 - U.S. Individual Income Tax Return](https://symagedocs.ai/forms/1040): Generate synthetic Form 1040 individual income tax returns with 139 fields of realistic financial data. Train document AI models on the most widely filed U.S. tax form with accurate cross-field dependencies between income, deductions, and tax calculations.
- [Form 1040 - U.S. Individual Income Tax Return](https://symagedocs.ai/forms/1040-1988): Generate synthetic 1988 Form 1040 individual income tax returns with period-accurate tax brackets (15%/28%/33%), standard deductions, and personal exemptions. Maximize layout diversity in training sets by combining with the 2024 version.
- [Form 1040-ES Estimated Tax for Individuals](https://symagedocs.ai/forms/1040-es): Generate synthetic Form 1040-ES estimated tax packets — the annualized worksheet plus four quarterly payment vouchers that repeat the same taxpayer identity. Training data for self-employed tax tooling, quarterly-estimate automation, and near-duplicate page detection.
- [Form 1040-NR - U.S. Nonresident Alien Income Tax Return](https://symagedocs.ai/forms/1040-nr): Generate synthetic Form 1040-NR nonresident alien returns with foreign addresses, ITIN-style identifiers, and treaty-position fields. Requesting this form switches the simulated population to nonresident-alien identities, so every generated return is a coherent cross-border filer.
- [Form 1040-SR - U.S. Tax Return for Seniors](https://symagedocs.ai/forms/1040-sr): Generate synthetic Form 1040-SR returns for taxpayers 65 and older — large-print layout, the age-65 additional standard deduction, and a retirement-weighted income mix. Every generated filer is age-gated at 65+, so the demographics behind the numbers are coherent rather than relabeled.
- [Form 1040-X - Amended U.S. Individual Income Tax Return](https://symagedocs.ai/forms/1040-x): Generate synthetic Form 1040-X amended returns with a real three-column A/B/C structure — original amount, signed net change, corrected amount — that reconciles line by line. Training data for amendment workflows, IRS-notice resolution, and signed-delta extraction.
- [Form 1065 - U.S. Return of Partnership Income](https://symagedocs.ai/forms/1065): Generate synthetic Form 1065 partnership returns across six pages of income, deductions, Schedule B yes/no questions, balance sheet, and book-to-tax reconciliation. Every generated partnership carries a realistic partner count, asset base, and receipts figure.
- [Form 1065-X - Amended Return or Administrative Adjustment Request](https://symagedocs.ai/forms/1065-x): Generate synthetic Form 1065-X amended partnership returns and administrative adjustment requests, built as real as-filed / net-change / corrected triples across five pages. The partnership counterpart to the 1040-X, with far denser currency columns.
- [Form 1120 - U.S. Corporation Income Tax Return](https://symagedocs.ai/forms/1120): Generate synthetic Form 1120 corporate income tax returns across six pages — income, deductions, tax computation, balance sheet, and book-to-tax reconciliation. The most computationally dense form in our catalog, with more arithmetic and conditional bindings than any other.
- [Form 1120-FSC - U.S. Income Tax Return of a Foreign Sales Corporation](https://symagedocs.ai/forms/1120-fsc): Generate synthetic Form 1120-FSC foreign sales corporation returns — six pages where every currency amount is split into separate dollar and cent columns. One of the densest forms in the catalog and the reference case for split-column numeric extraction.
- [Form 1120-H - U.S. Income Tax Return for Homeowners Associations](https://symagedocs.ai/forms/1120-h): Generate synthetic Form 1120-H returns for homeowners associations — exempt function income, association expenditures, and the 60% / 90% election tests on a single dense page. Training data for HOA accounting, community-association management, and property-tech document pipelines.
- [Form 1120-POL - U.S. Income Tax Return for Certain Political Organizations](https://symagedocs.ai/forms/1120-pol): Generate synthetic Form 1120-POL returns for political organizations — taxable investment income, deductions, and the specific-deduction computation on one page. Training data for campaign-finance compliance tooling and exempt-organization document pipelines.
- [Form 1120-SF - U.S. Income Tax Return for Settlement Funds under Section 468B](https://symagedocs.ai/forms/1120-sf): Generate synthetic Form 1120-SF returns for section 468B settlement funds — investment income, administrative expenses, and distributions, with every amount split into separate dollar and cent columns. Training data for settlement administration and legal-finance document pipelines.
- [Form 4868 - Application for Automatic Extension of Time to File](https://symagedocs.ai/forms/4868): Generate synthetic Form 4868 individual extension requests — the smallest tax form in our catalog, four PDF pages of which only two carry fields. The short-document case every tax classifier needs and almost no training corpus includes.
- [Form 7004 - Application for Automatic Extension](https://symagedocs.ai/forms/7004): Generate synthetic Form 7004 business extension requests, including the two-digit form code printed one character per box that identifies which return is being extended. Compact on the page, deepest binding logic in the catalog.
- [Form 709 - United States Gift (and Generation-Skipping Transfer) Tax Return](https://symagedocs.ai/forms/709): Generate synthetic Form 709 gift and generation-skipping transfer tax returns — five pages of donee schedules and tax computation where the great majority of fields are legitimately blank. The sparsest form in our catalog and the best available blank-field robustness corpus.
- [Form 940 - Employer's Annual Federal Unemployment (FUTA) Tax Return](https://symagedocs.ai/forms/940): Generate synthetic Form 940 FUTA tax returns with realistic employer payroll data and state unemployment tax calculations. Train document AI models on payroll tax forms with multi-state wage allocation fields.
- [Form 940-B - Request for Verification of Credit Information Shown on Form 940](https://symagedocs.ai/forms/940-b): Generate synthetic Form 940-B credit verification requests — the IRS-to-state-agency letter that asks a state unemployment office to confirm the FUTA credit an employer claimed on Form 940. Correspondence-style layout with per-state experience-rate periods and a three-bucket contribution payment timeline.
- [Form 940 Schedule R - Allocation Schedule for Aggregate Form 940 Filers](https://symagedocs.ai/forms/940-schedule-r): Generate synthetic Form 940 Schedule R aggregate allocation schedules — 596 fields of multi-client FUTA payroll spread across a 15-row primary grid and a 22-row continuation page. A pure table-extraction workload with dollar and cent columns annotated separately.
- [Form 941 - Employer's Quarterly Federal Tax Return](https://symagedocs.ai/forms/941): Generate synthetic Form 941 quarterly payroll tax returns with realistic wages, tips, withholding, and deposit schedule data. One of the most frequently filed employer tax forms, making it essential training data for payroll document AI.
- [Form 941 Schedule D - Report of Discrepancies Caused by Acquisitions, Statutory Mergers, or Consolidations](https://symagedocs.ai/forms/941-schedule-d): Generate synthetic Form 941 Schedule D discrepancy reports — the schedule that explains why the wages an employer reported to the IRS do not match what it reported to the Social Security Administration after a merger, acquisition, or consolidation. Every wage line appears as an IRS amount, an SSA amount, and a signed difference.
- [Form 941 Schedule R - Allocation Schedule for Aggregate Form 941 Filers](https://symagedocs.ai/forms/941-schedule-r): Generate synthetic Form 941 Schedule R aggregate allocation schedules — 828 fields, the largest form in the SymageDocs library, laid out as 26 client rows of quarterly payroll allocation with every money column split into dollars and cents. Built for benchmarking table extraction at scale.
- [Form 941-SS - Employer's Quarterly Federal Tax Return (American Samoa, Guam, CNMI, USVI)](https://symagedocs.ai/forms/941-ss): Generate synthetic Form 941-SS quarterly employer returns for American Samoa, Guam, the Northern Mariana Islands, and the U.S. Virgin Islands. Visually near-identical to the mainland Form 941 but with the federal income tax withholding line removed — the classic near-duplicate classification trap.
- [Form 941-X - Adjusted Employer's Quarterly Federal Tax Return or Claim for Refund](https://symagedocs.ai/forms/941-x): Generate synthetic Form 941-X adjusted quarterly payroll returns — 375 fields across five pages of corrections, refund claims, and payroll tax credit adjustments. Every corrected line prints as a corrected amount, an originally reported amount, and a signed difference.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2): Generate synthetic W-2 Wage and Tax Statements with realistic employer data, wage amounts, and tax withholdings — the single most common document in U.S. income verification. Each W-2 is tied to a coherent simulated identity whose wages, withholding, and state tax are internally consistent, and Box 3 respects the 2025 Social Security wage base.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-2026): Generate synthetic 2026 W-2 Wage and Tax Statements with updated Box 14a/14b split for Treasury Tipped Occupation Codes. Includes realistic employer data, wage amounts, and tax withholdings using the 2026 Social Security wage base of $184,500.
- [W-4 Employee's Withholding Certificate](https://symagedocs.ai/forms/w-4): Generate synthetic W-4 Employee's Withholding Certificates with realistic filing status selections, dependent claims, and withholding adjustments. Essential training data for payroll onboarding document processing pipelines.
- [W-9 Request for Taxpayer Identification Number and Certification](https://symagedocs.ai/forms/w-9): Generate synthetic W-9 taxpayer identification forms with realistic name, address, SSN, and federal tax classification data. A widely used form for vendor onboarding and contractor management document AI training.
- [I-9 Employment Eligibility Verification](https://symagedocs.ai/forms/i-9): Generate synthetic Form I-9 Employment Eligibility Verification packets with a coherent citizenship attestation in Section 1 and a matching List A or List B+C document set in Section 2. Every generated I-9 pairs a simulated new hire with an employer representative, a first day of employment, and identity documents that are consistent with the status the employee attested to.
- [WH-380-E Certification of Health Care Provider for Employee's Serious Health Condition](https://symagedocs.ai/forms/wh-380e): Generate synthetic WH-380-E FMLA medical certifications for an employee's own serious health condition, with a coherent employee, employer, treating provider, and clinical narrative. Each certification pairs a simulated worker and job description with a provider specialty and condition profile that actually fit each other.
- [WH-380-F Certification of Health Care Provider for Family Member's Serious Health Condition](https://symagedocs.ai/forms/wh-380f): Generate synthetic WH-380-F FMLA medical certifications for a family member's serious health condition, with a named patient, a stated care relationship, and a provider specialty that fits the diagnosis. Structurally the sparsest form in the FMLA family and the classic adversarial partner to the WH-380-E.
- [WH-381 Notice of Eligibility and Rights & Responsibilities (FMLA)](https://symagedocs.ai/forms/wh-381): Generate synthetic WH-381 FMLA Notice of Eligibility and Rights & Responsibilities documents — the employer-issued half of a leave file. Each notice records the leave reason, the eligibility determination, the certification deadline, the paid-leave substitution rules, and the reporting requirements the employer imposed.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-4-up-2024): Generate synthetic 2024 four-up W-2 sheets — the quarter-sheet layout that prints an employee's Copy B, Copy C and both Copy 2 forms on one page. Every copy on the sheet carries the same wage figures for the same employee, which makes this the layout for training copy-identification and near-duplicate handling.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-4-up-2025): Generate synthetic 2025 four-up W-2 sheets — the current-year quarter-sheet layout with the employee's four copies on one page. The last four-up revision before the 2026 Box 14 split, and the counterpart to the 2024 sheet in any two-year income lookback.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-reissued-2019): Generate synthetic reissued 2019 W-2 statements in the borderless quarter-sheet layout — the replacement copy an employer prints when an employee loses the original. The oldest revision in the catalog and the only W-2 rendered without printed box outlines.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-vertical-2023): Generate synthetic 2023 vertical-stack W-2 sheets — four full-width copies stacked top to bottom on one page. The oldest of the three stacked revisions, with the 2023 Social Security wage base, for testing whether a W-2 model holds up on a two-year-old document.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-vertical-2024): Generate synthetic 2024 vertical-stack W-2 sheets — four full-width copies stacked down one page at identical horizontal offsets. The layout that strips horizontal position out of the copy-disambiguation problem, with the 2024 Social Security wage base.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-vertical-2025): Generate synthetic 2025 vertical-stack W-2 sheets — four stacked copies on a two-page template whose second page carries no fields at all. Current-year wage base, plus the field-free trailing page that breaks naive page-classification and page-grouping logic.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-packet-2025): Generate synthetic 2025 W-2 documents in the full eleven-page official IRS packet, where every field lives on one interior page and the other ten are copies and instructions. The page-routing benchmark for multi-page document pipelines.
- [W-2 Wage and Tax Statement](https://symagedocs.ai/forms/w-2-packet-2026): Generate synthetic 2026 W-2 documents in the eleven-page official IRS packet — the current revision, with Box 14 split into 14a Other and 14b Treasury Tipped Occupation Codes. The regulatory change most 2026 extraction pipelines have not been retrained for.
- [SBA Form 413 Personal Financial Statement](https://symagedocs.ai/forms/sba-413): Generate synthetic SBA Form 413 Personal Financial Statements — a six-page personal balance sheet where summary totals on page one must reconcile against detail schedules three pages later. Training data for SBA lending and small-business underwriting automation.
- [SBA Form 2202 Schedule of Liabilities](https://symagedocs.ai/forms/sba-2202): Generate synthetic SBA Form 2202 Schedules of Liabilities — a twelve-row debt table whose occupancy decays from every row filled to one document in eight. Variable-length table extraction with ground truth, for SBA and disaster-loan underwriting.
- [Patient Demographic Form (New Patient Registration)](https://symagedocs.ai/forms/patient-registration): Generate synthetic new-patient registration and demographic forms — one dense page carrying three Social Security numbers, two insurance blocks and 38 checkboxes in mutually exclusive groups. Training data for healthcare intake automation and RCM front-end capture.
- [Professional Superbill (Itemized Medical Bill)](https://symagedocs.ai/forms/itemized-medical-bill): Generate synthetic itemized medical bills and professional superbills — service lines, diagnosis pointers and a charge-to-balance payment waterfall that reconciles on every document. Training data for medical-bill review, claims processing and payment integrity.
- [Invoice](https://symagedocs.ai/forms/invoice-construction): Generate synthetic contractor and trade invoices — seven line items, a subtotal-to-total tax chain, and a full remittance block with bank routing and account number. Training data for AP automation and vendor-payment fraud detection.
- [MA Verification of Self-Employment Income (INVF)](https://symagedocs.ai/forms/self-employment-income-verification): Generate synthetic self-employment income verification forms — the two-page declaration a benefits agency uses to verify gig and cash-economy earnings that no W-2 or 1099 covers. Training data for public-benefits eligibility automation.
- [Onboarding — Employee Personal Information](https://symagedocs.ai/forms/onboarding-personal-information): Generate synthetic employee personal information forms — the new-hire sheet carrying legal name, date of birth, Social Security number and home address that seeds every HRIS record downstream.
- [Onboarding — Emergency Contact](https://symagedocs.ai/forms/onboarding-emergency-contact): Generate synthetic employee emergency contact forms — two near-identical contact blocks plus physician and medical notes, the HR document that quietly carries health information.
- [Onboarding — Direct Deposit Authorization](https://symagedocs.ai/forms/onboarding-direct-deposit): Generate synthetic direct deposit authorization forms — two account blocks with routing and account numbers, split-deposit logic, and the exact document shape used in payroll-diversion fraud.
- [Onboarding — Employee Handbook Acknowledgment](https://symagedocs.ai/forms/onboarding-handbook-acknowledgment): Generate synthetic employee handbook acknowledgment forms — a short attestation page of policy checkboxes and a signature, and the sparsest document classification target in the catalog.
- [Onboarding — Benefits Enrollment](https://symagedocs.ai/forms/onboarding-benefits-enrollment): Generate synthetic benefits enrollment forms — medical plan and coverage tier, dental and vision elections, per-paycheck premiums and a Section 125 authorization, with a conditional cascade that blanks on waiver.
- [Onboarding — 401(k) Election](https://symagedocs.ai/forms/onboarding-401k-election): Generate synthetic 401(k) salary deferral election forms — enroll or decline, pre-tax or Roth, a deferral percentage, catch-up eligibility and auto-escalation, with an entire section that blanks on decline.
- [Onboarding — Equal Employment Opportunity Data](https://symagedocs.ai/forms/onboarding-eeo-data): Generate synthetic EEO self-identification forms — voluntary gender and race or ethnicity checkboxes feeding EEO-1 reporting, with the severe class imbalance real demographic data actually has.
- [Onboarding — Beneficiary Designation](https://symagedocs.ai/forms/onboarding-beneficiary-designation): Generate synthetic beneficiary designation forms — two pages carrying twelve Social Security numbers and fourteen dates of birth across spouse, children, parents and named beneficiaries, gated on family structure.

## Use Cases

Discover how ML teams use SymageDocs synthetic document data for OCR training, Document AI, HIPAA-compliant testing, KYC verification, and fraud detection.

- [Use Cases](https://symagedocs.ai/use-cases)
- [OCR Training Data Generation](https://symagedocs.ai/use-cases/ocr-training-data): Generate thousands of filled forms with pixel-perfect ground truth labels for training OCR and document extraction models.
- [Training Data for Document AI Models](https://symagedocs.ai/use-cases/document-ai-training): Fine-tune Google Document AI, Azure AI Document Intelligence, or custom models with diverse, labeled synthetic documents.
- [HIPAA-Compliant Synthetic Test Data](https://symagedocs.ai/use-cases/hipaa-compliant-test-data): Test healthcare claims processing pipelines with synthetic patient data that contains no real PII.
- [KYC & Identity Verification Test Data](https://symagedocs.ai/use-cases/kyc-test-data): Test identity verification pipelines with realistic but safe synthetic identities and corroborating documents.
- [Synthetic Data for Fraud Detection](https://symagedocs.ai/use-cases/fraud-detection-training-data): Generate both valid and intentionally inconsistent synthetic forms to train fraud detection models.
- [AI Pipeline Dev & QA Test Data](https://symagedocs.ai/use-cases/ai-pipeline-dev-qa): Replace production data in dev and staging environments with realistic synthetic documents.
- [Synthetic Training Data for Document AI](https://symagedocs.ai/use-cases/synthetic-training-data-for-document-ai): Synthetic training data for document AI: pixel-perfect ground truth for YOLOv8, LiLT, Donut, BIO NER, and FUNSD. Thousands of labeled documents in minutes.
- [Synthetic Training Data for YOLOv8 Document Detection](https://symagedocs.ai/use-cases/yolov8-document-detection-training-data): Generate unlimited synthetic training data for YOLOv8 document region detection. Paste-ready labels.txt, data.yaml, and class maps — no manual annotation.
- [BIO Tagged Synthetic NER Training Data](https://symagedocs.ai/use-cases/synthetic-ner-training-data-bio-format): Generate BIO-tagged synthetic NER training data in CoNLL, JSONL, and HuggingFace formats. Privacy-safe IOB2 datasets at any scale for document NER models.
- [Synthetic Training Data in FUNSD Format](https://symagedocs.ai/use-cases/synthetic-training-data-funsd-format): Generate unlimited FUNSD format synthetic data with question/answer linking and word bboxes. A drop-in alternative to FUNSD's 199 forms for LayoutLMv3 and LiLT.
- [Synthetic Training Data for LiLT](https://symagedocs.ai/use-cases/synthetic-training-data-for-lilt): Generate LiLT-ready fine-tuning data — tokens, 0-1000 normalized bboxes, and BIO labels — from synthetic business forms. Paste-ready Hugging Face snippets.
- [Synthetic Training Data for Donut (OCR-Free)](https://symagedocs.ai/use-cases/synthetic-training-data-for-donut): Fine-tune Donut with schema-rich synthetic training data. Real business form layouts paired with the structured JSON Donut's decoder is meant to emit.
- [Donut vs LiLT vs LayoutLM for Invoices](https://symagedocs.ai/use-cases/donut-vs-lilt-vs-layoutlm): Donut vs LiLT vs LayoutLM for invoices and forms: architecture, input formats, F1 benchmarks, and when to pick each for document understanding.
- [Tonic.ai Alternative for Document Training Data](https://symagedocs.ai/use-cases/tonic-ai-alternative): Tonic.ai de-identifies and synthesizes data you already have. SymageDocs generates filled, labeled document images from scratch. An honest comparison for document AI teams.
- [Gretel Alternative for Synthetic Document Data](https://symagedocs.ai/use-cases/gretel-alternative): Gretel's self-serve platform is gone — its technology lives on inside NVIDIA NeMo. If what you actually need is document-shaped synthetic data with labels, here's the honest comparison.
- [Faker vs Synthetic Document Data](https://symagedocs.ai/use-cases/faker-vs-synthetic-document-data): Faker and Mockaroo generate values; document AI needs documents. An honest comparison of random field generators vs coherent, labeled synthetic documents.
- [SynthDoG Alternative for Donut Fine-Tuning](https://symagedocs.ai/use-cases/synthdog-alternative): SynthDoG is Donut's pre-training generator. Fine-tuning on real business schemas needs structured JSON targets and field linking — here's how to fill that gap.

## Pricing

- [Pricing](https://symagedocs.ai/pricing): Start free with 500 credits/month. Scale to Pro, Scale, or Enterprise plans for synthetic identity and document generation.

## Free Sample Pack

- [Free Synthetic Document Dataset Download — Sample Pack](https://symagedocs.ai/sample-pack): Download a free synthetic document dataset: 10 coherent identities across 3 form types — 30 filled documents with FUNSD, BIO, YOLO and COCO annotations. Fully synthetic, no real PII, no signup required.

## Blog

Insights on synthetic document data, OCR training, Document AI, and privacy-safe ML workflows from the SymageDocs team.

- [Blog](https://symagedocs.ai/blog)
- [Invoice Extraction Fails on the Template You Didn't Train On](https://symagedocs.ai/blog/synthetic-invoice-data-layout-invariant-extraction): Invoice extraction breaks on templates it was not trained on. Train layout-invariant models with synthetic invoice data and a held-out-template eval.
- [Generate a Synthetic FUNSD Dataset for LayoutLMv3 Fine-Tuning](https://symagedocs.ai/blog/synthetic-funsd-dataset-layoutlmv3): Install the SymageDocs SDK, generate labeled synthetic W-2s, and convert the FUNSD annotations into LayoutLMv3 training features. Runs on the free tier.
- [Why Your OCR Model Degrades on Handwriting](https://symagedocs.ai/blog/ocr-handwriting-degradation): Your model hits 94% character accuracy on printed text and 61% on handwritten fields. This isn't a model architecture problem. It's a training data distribution problem — and once you see it clearly, the fix is straightforward.
- [What Is Synthetic Document Data and Why Does It Make Better Training Sets Than Real Records?](https://symagedocs.ai/blog/what-is-synthetic-document-data): A practical guide to how synthetic document data works, what makes it structurally different from anonymized or augmented real data, and when your ML pipeline actually needs it.
- [Why Faker Isn't Enough for Document AI Training](https://symagedocs.ai/blog/why-faker-isnt-enough-for-document-ai): Random data generators like Faker produce independent field values with no structural coherence. Here's why that matters for document extraction models, and what to use instead.

## Docs

- [API documentation](https://symagedocs.ai/docs/api)
- [Terms of Service and Output License](https://symagedocs.ai/terms)
- [Privacy Policy](https://symagedocs.ai/privacy)
- [Contact](https://symagedocs.ai/contact): Get in touch with SymageDocs support or contact our enterprise sales team.