WFGY/ProblemMap/GlobalFixMap/DocumentAI_OCR

Document AI & OCR — Global Fix Map

🏥 Quick Return to Emergency Room

You are in a specialist desk.
For full triage and doctors on duty, return here:

• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a sub-room.
If you want full consultation and prescriptions, go back to the Emergency Room lobby.

A beginner-friendly hub to stabilize OCR (Optical Character Recognition) and document AI pipelines across providers and open-source stacks.
This page helps you:

1. Understand common OCR failures.
2. Jump directly to per-tool guides.
3. Apply structural WFGY fixes with measurable acceptance targets.

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📌 When to use this folder

Use this map if you see any of these problems:

• OCR extracts text but loses tables or column alignment.
• Words are captured but semantic grouping is wrong (paragraphs broken).
• Citations don’t match the original scanned page.
• Layout-aware models drift after format changes (e.g. headers, forms).
• Two-column PDFs or rotated scans break retrieval.
• Cloud OCR services return different JSON fields each run.

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🎯 Acceptance targets for OCR systems

Think of these as “green lights” after your OCR step:

• ΔS(question, extracted text) ≤ 0.45 (semantic match stays tight).
• Coverage ≥ 0.70 of target section or table.
• λ stays convergent across 3 paraphrases and 2 random seeds.
• E_resonance stays flat across long documents (no drifting answers).

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🚀 Quick routes — per-provider guides

Provider / Tool	Open this guide
Tesseract (open-source OCR)	tesseract.md
Google Document AI	google_docai.md
AWS Textract	aws_textract.md
Azure OCR	azure_ocr.md
ABBYY (enterprise OCR)	abbyy.md
PaddleOCR (open-source)	paddleocr.md

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🛠️ Common symptoms → exact fixes

Symptom	Likely cause	Fix page
High similarity but wrong snippet	Embeddings confuse words with meaning	embedding-vs-semantic.md
Citations don’t line up with scanned region	Missing traceability or weak schema	retrieval-traceability.md · data-contracts.md
Multi-column / rotated pages fail	Chunking instability	chunking-checklist.md
Wrong OCR version after deploy	Boot ordering or pre-deploy collapse	bootstrap-ordering.md · predeploy-collapse.md
OCR+Vision hybrid worse than single	Query parsing split issue	pattern_query_parsing_split.md

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✅ 60-second fix checklist

1. Run OCR twice (two providers or seeds) → compare ΔS & λ.
2. Validate JSON schema → enforce {page_id, bbox, text, confidence}.
3. De-rotate scans, split multi-column before embedding.
4. Confirm coverage ≥ 0.70 on a gold page.
5. Force “cite then explain” in downstream reasoning steps.

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❓ FAQ (beginner-friendly)

Q: What is ΔS and why should I care?
ΔS measures semantic drift — if it’s above 0.45, your OCR text no longer matches the question well. Keep it lower to ensure stable answers.

Q: What does λ mean in practice?
λ checks consistency across paraphrases. If the system gives different answers for re-phrased questions, λ is unstable.

Q: Why do my citations not match the scanned PDF?
Usually because the OCR JSON has no stable IDs or coordinates. Fix by enforcing traceability fields like page_id and bbox.

Q: My OCR works on simple PDFs but fails on forms or invoices. Why?
That’s a chunking issue. Multi-column and rotated layouts need pre-processing before feeding to embeddings.

Q: Do I need to switch providers if accuracy is low?
Not always. Most errors come from pipeline design (chunking, contracts, retrieval) rather than the OCR engine itself.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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