What if we standardize open-source AI OCR training data?

Jung Wook Yang

sadpig70

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We’re entering a weird / exciting new moment in AI.

OCR is no longer “read this receipt.” With modern vision-language models, OCR is basically:

• compress entire documents (text, layout, tables, math, diagrams)
• turn that into compact visual tokens
• feed that into an AI as long-term context / memory

In other words: OCR data is becoming core memory infrastructure for AI.

Here’s the problem:

• High quality OCR training data is locked inside big companies.
• Public researchers, startups, schools, and most governments can't access it.
• Privacy concerns make it hard to share raw scanned documents.
• There's no agreed format for annotations, layout structure, reading order, or long-context memory.

Proposal:

1. We create an open, legal, privacy-safe OCR dataset standard.

   • Only legally safe sources (public docs, synthetic documents, patents, manuals, generated invoices, etc.).
   • Zero personal data, zero internal company secrets.

2. We define a shared annotation schema.

   • Page image
   • Structured text ground truth
   • Layout blocks (paragraphs, tables, equations, stamps, signatures, etc.)
   • Reading order
   • Table/Math structure
   • Optional “compressed visual tokens” representation for long-context memory models

3. We ship an open benchmark + leaderboard.

   • Anyone can test: text extraction, table reconstruction, math fidelity, long-document recall.
   • Fair comparison without paywall.

Why this matters:

• This lets every lab on Earth train high-quality document understanding models, not just a few trillion-dollar players.
• Governments and public institutions could build their own assistants that understand forms, regulations, scientific reports, etc.
• AI memory systems based on document snapshots become auditable and replicable.

This is not “let’s leak private PDFs.” This is “let’s build a global clean dataset standard so AI doesn’t become centralized forever.”

If Hugging Face hosts:

• v0.1 spec for the schema (fields, JSON, licenses)
• starter synthetic dataset (multi-language)
• evaluation scripts + baseline model

…then we basically set the norm for how AI should learn from documents safely.

Is anyone already working on this in the open? If not, who wants in?