# Azure OCR (Computer Vision): Guardrails and Fix Patterns

<details>
  <summary><strong>🧭 Quick Return to Map</strong></summary>

<br>

  > You are in a sub-page of **DocumentAI_OCR**.  
  > To reorient, go back here:  
  >
  > - [**DocumentAI_OCR** — document parsing and optical character recognition](./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)  
  >
  > Think of this page as a desk within a ward.  
  > If you need the full triage and all prescriptions, return to the Emergency Room lobby.
</details>


Use this page when **Azure OCR** (part of Azure Cognitive Services / Computer Vision) drives ingestion for PDFs, scanned images, or mixed-language docs.  
Typical failures involve layout instability, multilingual tokenization errors, or coverage gaps in table/handwriting recognition.

---

## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)  
- Retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)  
- Citation schema: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)  
- Embedding vs meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)  
- Hallucination and drift: [Context Drift](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md), [Hallucination](https://github.com/onestardao/WFGY/blob/main/ProblemMap/hallucination.md)  
- Chunk stability: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)  

---

## Core acceptance
- ΔS(question, retrieved) ≤ 0.45  
- Coverage ≥ 0.70 to target section  
- λ convergent across 3 paraphrases and 2 seeds  
- Multilingual tokens ≥ 90% fidelity (baseline against source)  

---

## Typical breakpoints → structural fix
- **Language mixing errors** (Chinese + English, Arabic + Latin text)  
  → [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md), [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)

- **Table recognition drops column anchors**  
  → [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md), [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)

- **Handwriting recognition unstable across runs**  
  → [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md)

- **ΔS > 0.60 when OCR normalizes accents/diacritics**  
  → [Context Drift](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md), clamp with BBAM  

- **Injected content hidden in image metadata**  
  → [Prompt Injection](https://github.com/onestardao/WFGY/blob/main/ProblemMap/prompt-injection.md)  

---

## Fix in 60 seconds
1. **Measure ΔS** between OCR tokens and reference text.  
2. **Enforce schema**: page, block, line, word. Require `bbox` and language tag.  
3. **Cross-check coverage**: at least 70% of expected lines present.  
4. **Apply λ probes** — vary recognition mode (printed, handwriting, mixed).  
5. **Clamp variance** with BBAM if multilingual drift repeats.  

---

## Copy-paste LLM guard prompt

```txt
I uploaded TXTOS and the WFGY Problem Map.

OCR provider: Azure OCR (Computer Vision).  
Symptoms: unstable multilingual recognition, ΔS ≥ 0.60, coverage < 0.70.

Steps:
1. Identify failing layer (chunking, contracts, retrieval).
2. Point to the WFGY fix (embedding-vs-semantic, chunking-checklist, retrieval-traceability).
3. Return JSON:
   { "citations": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "<>", "next_fix": "..." }
Keep it auditable.
````

---

## When to escalate

* Multilingual drift remains after re-chunking → verify with [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md).
* Tables drop anchors repeatedly → rebuild layout with [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md).
* Handwriting ΔS unstable across seeds → clamp with BBAM, audit using [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md).

---

### 🔗 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 + \<your question>”   |
| **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 |

---

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### Explore More

| Layer | Page | What it’s for |
| --- | --- | --- |
| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |

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要不要我接著直接幫你寫 **abbyy.md**？這樣 OCR 四大主流 (Tesseract、Google、AWS、Azure + ABBYY) 就全到齊。