WFGY/ProblemMap/GlobalFixMap/OCR_Parsing/images_and_figures.md

Images and Figures: 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

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.

Stabilize text extraction around inline images, charts, and figures. Prevent figure captions or axis labels from bleeding into body text, and preserve semantic anchors for later retrieval.

Open these first

• OCR end to end checklist: ocr-parsing-checklist.md
• Snippet and citation schema: data-contracts.md
• Retrieval traceability: retrieval-traceability.md
• Chunking checklist: chunking-checklist.md

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45 on captioned answers
• Coverage ≥ 0.70 for questions tied to figure anchors
• λ remains convergent across three paraphrases
• Captions and labels stored separately from body text

---

Typical failure signatures → fix

• Figure captions merged with paragraphs
  Split by bbox banding and assign figure_caption. Keep paragraph tokens clean.

• Axis labels or legend entries treated as running text
  Extract into figure_metadata.axis_labels and figure_metadata.legend. Never merge into narrative.

• Scanned figure with embedded text
  Route figure OCR separately. Keep figure_id, text_extracted, and bounding box. Tie back to figure image reference.

• Multi-column figure bleed
  If caption spans columns, capture as caption block, not as content. Anchor to figure_id.

• Images with no OCR text
  Provide stub with figure_id, bbox, and alt_text if known. Maintain traceability.

---

Fix in 60 seconds

1. Detect figure zones
   Identify bounding boxes flagged as images or graphics. Assign figure_id.

2. Isolate captions
   If text appears immediately above/below the figure and repeats formatting (italic, smaller font), tag as caption.

3. Route labels
   Apply heuristic rules for x-axis, y-axis, legend. Store under figure_metadata.

4. Clean narrative
   Remove all figure-related text from text_clean. Retain only in figure structures.

5. Probe retrieval
   Ask a figure-specific question. If ΔS ≤ 0.45 and λ stable, cleanup succeeded.

---

Minimal recipes by engine

• Google Document AI
  Use layout.figure and boundingPoly. Capture associated paragraph blocks as captions when within ±10% of figure bbox.

• AWS Textract
  Detect BlockType=KEY_VALUE_SET around figure images. Treat them as labels, route into figure_metadata.

• Azure OCR
  Use boundingRegions and detect blocks adjacent to figures. Anchor captions if directly above/below polygon.

• ABBYY
  In XML, <block type="Picture"> + following <par> → caption. Inline text with picture coordinates goes to figure metadata.

• PaddleOCR
  Split text lines overlapping with figure bbox. Store separately as figure text, not narrative.

---

Data contract additions for figures

{
"figure\_id": "fig3",
"bbox": \[x0,y0,x1,y1],
"caption": "Figure 3: Error rate across embedding sizes.",
"figure\_metadata": {
"axis\_labels": \["tokens","ΔS"],
"legend": \["baseline","with WFGY"],
"text\_extracted": "0.45, 0.30..."
},
"section\_id": "4.2",
"page": 12,
"source\_url": "..."
}

Mandatory: all figure text lives in figure_metadata or caption, never in text_clean.

---

Verification

• Leak check: ensure no caption/axis strings appear in text_clean.
• Figure QA: ask "what does fig3 show?" — answer must cite figure_id.
• ΔS probe: figure-specific questions yield ΔS ≤ 0.45.
• λ probe: paraphrases about same figure converge.

---

Copy-paste LLM prompt

You have TXT OS and WFGY Problem Map.

For figure-linked snippets:

* use text\_clean for reasoning,
* use caption and figure\_metadata for figure answers,
* cite figure\_id.

Tasks:

1. If figure text leaks into body, fail fast and return fix reference (ocr-parsing-checklist, data-contracts, retrieval-traceability).
2. Return JSON:
   { "citations":\["fig3"], "answer":"...", "λ\_state":"...", "ΔS":0.xx, "next\_fix":"..." }

---

🔗 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 based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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