# Multi-language and Fonts: OCR Parsing Guardrails
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> - [**OCR_Parsing** — text recognition and document structure parsing](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/ocr-parsing-checklist.md)
- Data contracts: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
- Tokenization and casing: [tokenization_and_casing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/OCR_Parsing/tokenization_and_casing.md)
- Unicode normalization: [normalization_and_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/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](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \” |
| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
---
### 🧭 Explore More
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
> 
⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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