Azure OCR (Computer Vision): Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of DocumentAI_OCR.
To reorient, go back here:

• DocumentAI_OCR — document parsing and optical character recognition
• 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 desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

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.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• Retrieval knobs: Retrieval Playbook
• Citation schema: Retrieval Traceability
• Embedding vs meaning: Embedding ≠ Semantic
• Hallucination and drift: Context Drift, Hallucination
• Chunk stability: Chunking Checklist

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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)

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Typical breakpoints → structural fix

• Language mixing errors (Chinese + English, Arabic + Latin text)
  → Embedding ≠ Semantic, Chunking Checklist

• Table recognition drops column anchors
  → Data Contracts, Retrieval Traceability

• Handwriting recognition unstable across runs
  → Entropy Collapse

• ΔS > 0.60 when OCR normalizes accents/diacritics
  → Context Drift, clamp with BBAM

• Injected content hidden in image metadata
  → Prompt Injection

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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.

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Copy-paste LLM guard prompt

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.

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When to escalate

• Multilingual drift remains after re-chunking → verify with Embedding ≠ Semantic.
• Tables drop anchors repeatedly → rebuild layout with Data Contracts.
• Handwriting ΔS unstable across seeds → clamp with BBAM, audit using Entropy Collapse.

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🔗 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

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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 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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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。