7 KiB
PaddleOCR: 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 your stack integrates PaddleOCR (from Baidu PaddlePaddle).
It’s widely used for open-source OCR pipelines, especially in Chinese / multilingual contexts.
Common risks: unstable detection boxes, segmentation drift, and mixed-language confusion.
Open these first
- Visual map and recovery: RAG Architecture & Recovery
- End-to-end knobs: Retrieval Playbook
- Traceability schema: Retrieval Traceability
- Schema contracts: Data Contracts
- Embedding vs meaning: Embedding ≠ Semantic
- Chunking rules: Chunking Checklist
- Injection risks: Prompt Injection
Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 across multilingual tokens
- λ convergent across 3 paraphrases
- BBox coverage ≥ 95% on gold set images
Typical breakpoints → structural fix
-
Chinese / English mix mis-segmented
→ Embedding ≠ Semantic, Data Contracts -
Bounding boxes drift (word cropped or merged incorrectly)
→ Retrieval Traceability, enforce field anchors -
Long text lines wrapped unpredictably
→ Chunking Checklist -
Injected noise from non-text graphics
→ Prompt Injection -
Low recall on handwriting or distorted fonts
→ Entropy Collapse
Fix in 60 seconds
- Normalize text direction (LTR vs RTL) before feeding embeddings.
- Apply schema:
bbox,text,lang,confidence,rev_id. - Measure ΔS(question, retrieved). Threshold ≥ 0.60 → suspect segmentation or index.
- Clamp λ with BBAM if paraphrases diverge.
- Re-chunk with stride windows for multilingual pages.
Copy-paste guard prompt
I uploaded TXTOS and the WFGY Problem Map.
OCR provider: PaddleOCR.
Symptoms: multilingual mis-segmentation, ΔS ≥ 0.60, bbox drift.
Steps:
1. Identify failing layer (chunk, retrieval, schema).
2. Point to correct WFGY page.
3. Return JSON:
{ "bbox_checked": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "<>", "next_fix": "..." }
Keep it short, reproducible, auditable.
When to escalate
- Persistent bbox drift → Retrieval Traceability
- Schema mismatch across languages → Data Contracts
- ΔS unstable across seeds → Embedding ≠ Semantic
🔗 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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