WFGY/ProblemMap/GlobalFixMap/OCR_Parsing/README.md

OCR + Parsing — 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 hub to triage and repair noisy text inputs from scanned PDFs, images, HTML scraping, or parser drift.
Use this folder when the document looks fine to the eye but retrieval or reasoning keeps failing.

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Orientation: what each page does

Page	What it solves	Typical symptom
Layout, Headers, Footers	Remove noise from margins and repeated text	Answers reference “page 3 footer” instead of body
Tokenization & Casing	Normalize Unicode, case, and hyphens	E-mail ≠ Email, half-width/full-width mismatch
Tables & Columns	Preserve table schema and cell order	Numbers drift across columns
Images & Figures	OCR and align captions	Figure text missing or attached to wrong section
Scanned PDFs & Quality	Handle skewed/blurred pages	Whole sections unreadable to OCR
Multi-language & Fonts	Normalize mixed scripts	Chinese/English tokens split or duplicated

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When to use

• OCR tables or citations look visually correct but answers miss the right section.
• Code blocks or math collapse after parsing.
• Mixed-language documents behave inconsistently.
• Special characters or hyphen splits break tokens.
• Headers or section anchors disappear during export.

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FAQ

Why does OCR “look fine” but retrieval fails?
Because tokenization and indexing see hidden breaks (Unicode variants, line merges, wrong anchors) that humans overlook.

What is the most common root cause?
Headers/footers leaking into the body and breaking ΔS alignment.

Do I need to retrain embeddings after fixing?
No — most fixes are structural (schema/normalization). Re-indexing with the same embeddings is enough.

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Acceptance targets

• ΔS(question, retrieved) ≤ 0.45 for three paraphrases.
• Coverage ≥ 0.70 for the target section.
• λ_observe convergent across two seeds.
• Human audit shows no missing headers, captions, or broken tables.

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Fix in 60 seconds

1. Ground-truth one page
   Pick one Q/A pair and keep a screenshot baseline.

2. Measure ΔS
   Log ΔS(question, retrieved) and ΔS(retrieved, anchor).

3. Probe λ_observe
   Ask for cite-first. If citation fails but free explanation works, drift confirmed.

4. Patch minimally

   • Re-run OCR with line/table fences
   • Normalize casing and Unicode
   • Preserve anchors, math, captions
   • Drop low-confidence spans and export with {section_id, page_no, char_span}

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload · 3️⃣ Ask “Answer using WFGY + ”
TXT OS	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into LLM · 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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