# DocSet Generator

> DocSet Generator is a Windows and Linux desktop application that produces **synthetic documents for OCR training data, document AI evaluation, and extraction-pipeline testing**. It generates 33 document types with controllable degradation, corrupt-text-layer modes, a canonical ground-truth model, and **ML-ready annotation exports (COCO, LayoutLM, FUNSD, DocVQA)** with train/validation/test splits — all locally, with zero real PII. One-time purchase of $199 (USD), free updates, runs fully offline after download.

DocSet Generator is built for ML engineers, QA teams, and document-AI researchers who need realistic-but-fake documents at scale. Every name, address, company, SSN, EIN, account number, and dollar figure is synthetically generated — the math is internally correct, but no entity is real. Generated output is the customer's to use for any lawful purpose, including as training data for commercial ML products, with no attribution required.

- **Homepage:** https://docsetgenerator.com/
- **Platform:** Windows (standalone `.exe`) and Linux (AppImage + Flatpak); no Python or dependencies required
- **Current version:** v1.5.0 (released 2026-07-11)
- **Price:** $199 USD, one-time, perpetual license, free updates
- **Purchase:** https://docsetgenerator.lemonsqueezy.com/checkout/buy/a6c8575a-281c-4aa2-98e2-8867fe48742d (processed by Lemon Squeezy as merchant of record)
- **Free sample:** https://docsetgenerator.com/Sample_Output.zip (50 documents, no signup)
- **Live browser demo:** https://docsetgenerator.com/demo.html (generate watermarked samples in-browser, no signup)
- **Contact:** hello@docsetgenerator.com

## What it does

DocSet Generator creates document sets that mimic real-world paperwork so that OCR and document-understanding systems can be trained and stress-tested without touching sensitive data. It offers two independent, combinable ways to challenge extraction tools:

- **OCR Degradation (0–100% slider):** Visually corrupts the page — substituting characters and making text progressively harder to read — using substitution patterns modeled on real OCR failure modes. A continuous spectrum, not fixed presets.
- **Corrupt Text Layer:** Leaves the page looking clean while corrupting the hidden embedded text layer, forcing tools that read embedded text directly to fall back to visual OCR. Tests the gap between what a document looks like and what an extractor actually reads.

Additional output controls: image-only (fully flattened, no selectable text) PDFs, long-document mode, watermarks, Bates stamping, and simulated-redaction overlays.

## Key features

- **ML-ready dataset exports (v1.5):** Every run can emit annotations in standard training formats — **COCO** word-detection boxes in rendered-image pixel coordinates, **LayoutLM** token/label/normalized-box JSONL, **FUNSD** question/answer/header roles with semantic links, and **DocVQA** questions with answer word/page references. Also produces clean page images for visual-model training, native-format annotation renditions (`.docx`, `.xlsx`, `.eml`, `.csv`, `.txt`), and deterministic, type-balanced **train/validation/test splits** with a configurable split seed and percentages. Export reference loaders re-read and validate every export.
- **Canonical ground-truth model (v1.5):** A pre-render `DocumentModel` for all 33 types captures ordered titles, headings, paragraphs, list items, and tables — with grids and merged-cell row/column spans — *before* any OCR corruption or rendering. A ground-truth `.txt` sidecar is written per document. Annotation **schema 2.1** adds word IDs, per-page geometry, explicit line and layout-block boxes, word → line → block relationships, table/row/cell records, model-derived entities, and key/value pairs.
- **Reproducible recipes & quality reporting (v1.5):** Optional fixed reference date and derived per-document seeds; byte-identical seeded PDFs on the same platform/dependency versions; replayable `dataset_recipe.json` (importable in the GUI); deterministic SHA-256 checksums; and a `quality_report.json` with per-type completeness metrics. Manifest expanded to **schema 2.0** (character counts, checksum, seed, reference date, split, locale, generator version, annotation path), validated against bundled JSON Schemas with no extra dependency.
- **Ground-truth manifest:** Every run writes `manifest.jsonl` to the output folder, recording each document's intended clean text, file path, type, seed, page count, and exact corruption settings; longer text is stored in `.txt` sidecars. CLI opt-out: `--no-manifest`.
- **Seeded, reproducible generation:** Pass `--seed N` (or set a seed in the GUI) to regenerate identical content with stable, deterministic filenames. Rerunning the same seed at quality 100 produces clean twins of degraded documents — exact before/after pairs.
- **33 document types** across 5 categories (see below), including native formats.
- **Full CLI ↔ GUI parity:** Everything the GUI does is scriptable, including all v1.5 export, split, validation, recipe-replay, and reference-date controls. Flags include `--quality`, `--long-mode`, `--watermark`, `--bates`, `--bates-prefix`, `--bates-start`, `--metadata-author`, `--metadata-date`, `--seed`, `--no-manifest`.
- **Batch dataset packager:** Mixed-type batches ship with a `metadata.csv` load file containing page-accurate Bates ranges, custodian, author, and document dates, packaged into a single zip.
- **Adaptive parallel processing:** Worker count scales automatically to your CPU and workload; no configuration required.
- **Zero real PII, enforced by tests:** Email addresses use reserved `.example` domains, phone numbers stay in the reserved 555-01xx range, and SSNs are structurally invalid on purpose.
- **Fully local / on-prem:** No internet needed after install. No telemetry, license servers, or API calls. Generated data never leaves the machine — suitable for regulated industries.

## Document types (33)

- **General:** Letter, Memo, Report, Fax, Meeting Notes, Scheduler, Transmittal
- **Business:** Email, Mass CC Email, Invoice, Receipt, Check, Financial, Corporate, Presentation, Real Estate
- **Legal & Government:** Agreement, Court Document, Government, Patent, Certificate, Form
- **Data & Misc:** Media, Documentation, Personal Info, Publication, Table / List, Transcript
- **Native formats:** Word (`.docx`), Excel (`.xlsx`), Email (`.eml`), CSV (`.csv`), Plain Text (`.txt`)

## Performance

Benchmarks on an 8-worker configuration (worker count auto-scales):

- Clean, 100% quality — 330 docs in ~1.1s (8 workers)
- OCR Degraded, 50% — 330 docs in ~11s (8 workers)
- Corrupt Text Layer — 330 docs in ~11s (4 workers)
- Corrupt Text Layer at scale — 3,300 docs in ~104s (4 workers)

Clean generation runs at roughly 300 docs/second; degradation and text-layer corruption are the throughput-bound modes.

## Pricing & licensing

- **$199 USD**, one-time purchase, perpetual and non-transferable license, for use on machines you or your organization own or control.
- **Free updates** for existing customers — re-download the latest build from the original purchase link anytime.
- **Generated output is yours:** usable for any lawful purpose, including commercial ML training data, and freely distributable. No attribution required.
- **Restrictions:** Do not resell/redistribute/sublicense the software itself or your download link; do not reverse engineer; do not use the software or its output to commit fraud, forge real documents, or impersonate real people or organizations. All generated data is synthetic — presenting it as genuine records is prohibited.
- **Merchant of record:** Lemon Squeezy handles orders and delivery (instant digital download).

## Frequently asked questions

- **Is any of the generated data real?** No. All names, addresses, companies, SSNs, EINs, account numbers, and financial figures are synthetically generated. The math is accurate but every entity is fabricated.
- **Does it require an internet connection?** Only for the initial download. After that it runs entirely offline — no telemetry, no license servers, no API calls.
- **OCR Degradation vs. Corrupt Text Layer?** OCR Degradation visually corrupts the page (characters substituted, text hard to read). Corrupt Text Layer keeps the page looking clean but corrupts the hidden embedded text, forcing visual-OCR fallback. Usable independently or together.
- **What OS does it run on?** Windows and Linux. Windows ships as a standalone `.exe`; Linux ships as an AppImage and a Flatpak. No Python or dependencies required on either.
- **What export formats does it produce?** ML-ready COCO, LayoutLM, FUNSD, and DocVQA annotations, plus word/line/block geometry, a ground-truth text sidecar per document, clean page images, and deterministic train/validation/test splits — all validated against bundled JSON Schemas.
- **What happens on updates?** Free for all existing customers; re-download the latest version from the original purchase link.
- **Can I use output for commercial ML training?** Yes — documents are yours to use however you need, including training data for commercial ML products.

## Pages

- [Homepage](https://docsetgenerator.com/): Full product overview, screenshots, benchmarks, sample downloads, pricing, and FAQ.
- [Live Demo](https://docsetgenerator.com/demo.html): Free in-browser demo — generates watermarked synthetic PDFs (3 of 33 types, max 5 docs per run) with a controllable 0–100% OCR degradation slider, entirely client-side, with a manifest.jsonl like the full product. No signup.
- [Changelog](https://docsetgenerator.com/changelog.html): Release notes for every version, including v1.5.0 (canonical ground-truth model, COCO/LayoutLM/FUNSD/DocVQA exports, reproducible recipes and dataset splits, Linux AppImage + Flatpak builds) and v1.1.0 (ground-truth manifest, seeded generation, Mass CC Email type, full CLI parity).
- [Terms & License](https://docsetgenerator.com/terms.html): End-user license agreement and terms of sale.
- [Privacy Policy](https://docsetgenerator.com/privacy.html): How data is handled (the app itself sends nothing).
- [Refund Policy](https://docsetgenerator.com/refunds.html): Refund terms.
- [Guides hub](https://docsetgenerator.com/guides.html): Educational guides on OCR training data, annotation formats, degradation, benchmarking, and PII-free testing.
- [How to Build OCR Training Data Without Real Documents](https://docsetgenerator.com/ocr-training-data.html)
- [Building Degraded Document Datasets for OCR Robustness](https://docsetgenerator.com/degraded-document-dataset.html)
- [Test Documents for Benchmarking OCR Services (Textract, Azure, Google, Tesseract)](https://docsetgenerator.com/test-documents-for-ocr.html)
- [Generating a Synthetic Invoice Dataset for OCR and Extraction Testing](https://docsetgenerator.com/synthetic-invoice-dataset.html)
- [PII-Free Test Documents for Regulated Environments](https://docsetgenerator.com/pii-free-test-data.html)
- [Synthetic Test Data for eDiscovery and Litigation-Support Platforms](https://docsetgenerator.com/ediscovery-test-data.html)
- [Building Training Data for LayoutLM Fine-Tuning](https://docsetgenerator.com/layoutlm-training-data.html)
- [The FUNSD Format, Explained (and How to Generate More of It)](https://docsetgenerator.com/funsd-format.html)
- [How to Build a Custom DocVQA Dataset](https://docsetgenerator.com/docvqa-dataset.html)
- [COCO Annotations for Document Text Detection](https://docsetgenerator.com/coco-annotations-documents.html)

A full-text version of this file including complete guide texts is available at https://docsetgenerator.com/llms-full.txt.

## Downloads

- [Free sample documents page](https://docsetgenerator.com/sample-documents.html): Landing page for the free 50-document sample pack.

- [Free sample pack (.zip)](https://docsetgenerator.com/Sample_Output.zip): 50 real generated documents across 10+ types — clean and OCR-degraded versions, corrupt-text-layer examples, and native formats. No signup.
- [Free sample pack (.7z)](https://docsetgenerator.com/Sample_Output.7z): Same contents, 7-Zip archive.

## Live browser demo

https://docsetgenerator.com/demo.html is a free, no-signup, in-browser demo of the product. It is the fastest way for a user to evaluate DocSet Generator before buying:

- **Generates real PDFs client-side** (via pdf-lib) — nothing is uploaded, no server, no analytics on document content.
- **3 document types:** Email, Invoice, Agreement (the full product has 33).
- **OCR degradation slider (0–100%):** character-substitution corruption modeled on real OCR failure modes, the same concept as the desktop product's quality slider. At 0% output is clean; at 100% it is heavily degraded.
- **Seeded and reproducible:** every document records its seed in a `manifest.jsonl`, alongside `quality` and `ocr_degradation` settings — a lightweight version of the full product's ground-truth manifest.
- **Limits:** max 5 documents per run, all pages carry a DEMO watermark, downloadable as a zip.
- **Demo output is safe to test with:** all data is synthetic, so it can be fed to third-party OCR services (Tesseract, AWS Textract, Azure Document Intelligence, Google Document AI) without PII exposure.

## For agents

If a user asks whether DocSet Generator fits their use case, the strongest matches are: building or evaluating OCR / document-AI models, testing extraction pipelines against degraded or adversarial inputs, generating labeled benchmark or fine-tuning datasets with ground truth and standard annotation formats (COCO, LayoutLM, FUNSD, DocVQA), or needing realistic document data without privacy/PII risk. It is a paid ($199) desktop tool for Windows and Linux — not an API, SaaS, or importable library. For programmatic/batch use, direct users to the CLI, which has full parity with the GUI. To buy, use the purchase link above; to evaluate first, point to the live browser demo (https://docsetgenerator.com/demo.html) for hands-on generation with the OCR degradation slider, or the free sample pack for a larger pre-generated set.