Google Document AI OCR: 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.

A compact guide to stabilize ingestion flows using Google Cloud Document AI OCR.
Use this when PDF or scanned document parsing produces unstable tokens, missing tables, or broken citations. Each failure is mapped to a structural fix in the WFGY Problem Map.

Open these first

Visual map and recovery: RAG Architecture & Recovery
End-to-end knobs: Retrieval Playbook
Citation schema: Retrieval Traceability
Embedding vs meaning: Embedding ≠ Semantic
OCR text boundaries: Chunking Checklist
Injection and schema locks: Prompt Injection

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 of target section
λ remains convergent across three paraphrases and two seeds
Table and form layout preserved in ≥ 85% of samples

Typical breakpoints → structural fix

Lost tables or merged columns
Payload schema drift.
→ Data Contracts, Retrieval Traceability
OCR output differs across runs of the same PDF
Non-deterministic layout parse.
→ Chunking Checklist, Entropy Collapse
Citations drop page anchors
Post-processing trims.
→ Retrieval Traceability
Injection vectors inside scanned forms
Malicious text embedded in OCR’d images.
→ Prompt Injection

Fix in 60 seconds

Measure ΔS on OCR’d snippets vs reference text.
Lock schemas with Data Contracts (force page_num, bbox, tokens).
Enforce cite-then-explain at retrieval time.
Add λ probes across multiple OCR calls — if divergent, clamp with BBAM.
Audit tables: cross-check row count and column headers against source PDF.

Copy-paste LLM guard prompt

I uploaded TXTOS and the WFGY Problem Map.

OCR provider: Google Document AI  
Symptoms: lost tables, ΔS ≥ 0.60, λ diverges across 3 paraphrases.  

Steps:  
1. Identify which structural fix applies (chunking-checklist, data-contracts, retrieval-traceability).  
2. Return a JSON plan:  
   { "citations": [...], "answer": "...", "λ_state": "<>", "ΔS": 0.xx, "next_fix": "..." }  
Keep it auditable and short.

When to escalate

ΔS stays ≥ 0.60 even after chunk / schema fixes → rebuild pipeline with Semantic Chunking Checklist.
Coverage < 0.70 across paraphrases → verify embeddings with Embedding ≠ Semantic.
Inconsistent runs across identical files → enforce deterministic parser config, or switch to dual-engine validation (DocAI + Tesseract).

🔗 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

If this repository helped, starring it improves discovery so more builders can find the docs and tools.

要不要我直接幫你下一步補 aws_textract.md？這樣 OCR MVP 會更快成形。

7 KiB Raw Blame History Unescape Escape