OCR Jitter — Guardrails and Fix Pattern

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WFGY Problem Map 1.0 — 16 reproducible failure modes

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When OCR engines process scanned text with inconsistent spacing, width variants, or mixed character forms,
the output may look visually correct but introduces false token differences that destabilize retrieval and reasoning.

Symptoms

OCR transcript looks fine to the eye, but semantic retrieval drifts.
Words alternate between half-width / full-width forms.
Invisible characters (zero-width joiners, non-breaking spaces) trigger token mismatches.
Capitalization inconsistent across the same word in long transcripts.
Citations fail even though the snippet visually matches the source.

Root causes

OCR confidence below threshold but output still accepted.
Normalization skipped (NFC vs NFD forms mixed).
Scanner artifacts (speckles, warped lines) inject invisible characters.
Language-specific width forms (CJK fullwidth vs ASCII halfwidth) untreated.
No post-processing pass to unify tokens before embedding.

Fix in 60 seconds

Gate by confidence
- Drop lines with OCR confidence < 0.85.
- Flag low-confidence tables and equations for manual review.
Normalize Unicode
- Convert to NFC form.
- Replace non-breaking spaces with plain space.
- Strip zero-width characters.
Unify width and case
- Map fullwidth and halfwidth characters consistently.
- Apply case-folding for ASCII text.
Re-stamp clean snippets
- After normalization, reassign line numbers.
- Ensure section_id | start_line | end_line | citation schema updated.
Verify joins
- Run ΔS across adjacent chunks.
- If join ΔS ≥ 0.50, suspect hidden jitter — repeat normalization.

Copy-paste diagnostic prompt

You have TXTOS and the WFGY Problem Map.

Task: Detect and repair OCR jitter.

Protocol:
1. Normalize all snippets:
   - Unicode NFC
   - Strip zero-width, NBSP
   - Map fullwidth → halfwidth
   - Apply case-fold
2. Drop snippets with OCR confidence < 0.85.
3. Re-stamp Snippet Table with {section_id, start_line, end_line, citation}.
4. Measure ΔS across adjacent chunks:
   - Target ≤ 0.50 at each join.
5. Report ΔS(question, retrieved) and λ states.

Acceptance targets

OCR confidence ≥ 0.85 for all retained lines.
No mixed width or hidden characters in final text.
ΔS(question, retrieved) ≤ 0.45 and joins ≤ 0.50.
λ remains convergent across three paraphrases.
Snippets traceable and citations reproducible.

🔗 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 + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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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