Guides

Synthetic Document Data, Explained

Practical guides on building OCR training data, testing extraction pipelines, annotation formats, and PII-free document test sets — written by the team behind DocSet Generator.

• Free Sample Documents for Testing
  Download 50 synthetic documents — clean/degraded pairs, corrupt text layers, native formats. No signup.
• How to Build OCR Training Data Without Real Documents
  A practical guide to building OCR training datasets with synthetic documents: ground truth, degradation, annotation formats, train/val/test splits, and why zero-PII matters.
• Building Degraded Document Datasets for OCR Robustness
  How to build datasets of degraded, noisy scanned documents for OCR robustness training and testing: realistic corruption models, clean/degraded twins, and the corrupt-text-layer technique.
• Test Documents for Benchmarking OCR Services
  How to build a test document set for benchmarking AWS Textract, Azure Document Intelligence, Google Document AI, and Tesseract — degraded scans, ground truth scoring, and zero PII risk.
• Generating a Synthetic Invoice Dataset for OCR and Extraction Testing
  How to generate realistic fake invoices, receipts, and financial documents for testing OCR and data-extraction pipelines — with internally consistent math and zero real PII.
• PII-Free Test Documents for Regulated Environments
  How to get realistic document test data with zero PII: why redaction and anonymization fail, what structurally-fake entities look like, and fully-offline synthetic generation for regulated industries.
• Synthetic Test Data for eDiscovery and Litigation-Support Platforms
  How to generate realistic eDiscovery test data: mixed-type document batches with Bates stamping, metadata.csv load files, custodians, native formats, and simulated redactions — zero real PII.
• Building Training Data for LayoutLM Fine-Tuning
  How to prepare LayoutLM training data: token/label/normalized-bounding-box JSONL structure, the 0–1000 coordinate convention, labeling pitfalls, and generating synthetic LayoutLM datasets.
• The FUNSD Format, Explained (and How to Generate More of It)
  What the FUNSD dataset format is, how its question/answer/header annotation schema works, its 199-document limitation, and how to generate unlimited synthetic FUNSD-format data.
• How to Build a Custom DocVQA Dataset
  What the DocVQA format looks like, why question-answer annotation over documents is expensive, and how to generate synthetic DocVQA training data with grounded answers.
• COCO Annotations for Document Text Detection
  How the COCO annotation format applies to document images and OCR text detection: images/annotations/categories structure, bbox conventions, and generating synthetic COCO document data.