WFGY/ProblemMap/GlobalFixMap/OCR_Parsing/multi_language_and_fonts.md

Multi-language and Fonts: OCR Parsing Guardrails

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• OCR_Parsing — text recognition and document structure parsing
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

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Stabilize OCR when documents mix scripts, uncommon fonts, or character sets. Prevent silent corruption when engines guess wrong language or merge glyphs across font families.

Open these first

• OCR parsing checklist: ocr-parsing-checklist.md
• Data contracts: data-contracts.md
• Tokenization and casing: tokenization_and_casing.md
• Unicode normalization: normalization_and_scaling.md

Acceptance targets

• Language detection accuracy ≥ 0.95 per block
• Font mis-read rate < 1% per 1,000 chars
• No cross-script merges (CJK vs Latin, RTL vs LTR)
• ΔS(question, retrieved) ≤ 0.45 after language split

---

Typical failure signatures → fix

• CJK vs Latin collisions
  OCR merges Latin letters inside Chinese/Japanese text. Split into script-specific blocks, then re-OCR with correct language model.

• Right-to-left scripts (Arabic, Hebrew) misaligned
  Store direction=rtl metadata. Reverse tokens if engine defaults to LTR.

• Uncommon fonts or stylized typefaces
  Preprocess with font normalization (convert to system fonts). Use OCR engine with adaptive recognition.

• Mixed languages in same paragraph
  Detect language per line or span. Store lang_code for each.

• Math vs text confusion
  Superscripts, subscripts, and symbols misinterpreted as language characters. Route math zones separately. Tag as math_block.

---

Fix in 60 seconds

1. Detect language per block
   Run script detection. Assign lang_code and direction. Reject ambiguous blocks.

2. Normalize Unicode
   Apply NFKC, collapse ligatures, unify spacing.

3. Re-OCR with correct model
   For each block, call OCR with explicit lang_code. Prefer specialized models (e.g., PaddleOCR multilingual, ABBYY).

4. Attach metadata
   Store lang_code, direction, font_name if available.

5. Audit with ΔS
   Probe retrieval stability with three paraphrases. If ΔS ≥ 0.60, recheck font normalization.

---

Data contract extension

{
"block\_id": "scan12\_line4",
"lang\_code": "zh",
"direction": "ltr",
"font\_name": "SimSun",
"text\_clean": "...",
"confidence": 0.93,
"source\_url": "..."
}

---

Minimal recipes by engine

• Google Document AI
  Use detectedLanguages.languageCode per block. Reject if confidence < 0.8.

• AWS Textract
  No native multi-lang. Wrap with external script detection. Add lang_code manually.

• Azure OCR
  language field auto-detected. Cross-check with Unicode ranges.

• ABBYY
  Supports per-block language tags. Ensure config has all needed languages.

• PaddleOCR
  Use multilingual model. Explicitly set --lang flag to avoid mis-guess.

---

Verification

• Script coverage: verify all scripts recognized.
• Direction check: RTL blocks labeled correctly.
• Font audit: ensure no decorative font corruption.
• Retrieval stability: ΔS stable across paraphrases.

---

Copy-paste LLM prompt

You have TXTOS and WFGY Problem Map loaded.

My OCR block:

* text\_clean: "..."
* lang\_code: "ar"
* direction: "rtl"
* font\_name: "Courier"

Check:

1. If characters look corrupted, fail fast and cite fix page.
2. Enforce schema with lang\_code and direction.
3. Return JSON: { "answer":"...", "citations":\[...], "ΔS":0.xx, "λ\_state":"..." }

---

🔗 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 tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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