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# Document AI & OCR — Global Fix Map
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A hub to stabilize OCR and document AI pipelines across providers and open-source stacks.
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Use this folder to jump to guardrails, check common breakpoints, and apply structural fixes with measurable targets.
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A **beginner-friendly hub** to stabilize OCR (Optical Character Recognition) and document AI pipelines across providers and open-source stacks.
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This page helps you:
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1. Understand common OCR failures.
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2. Jump directly to per-tool guides.
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3. Apply structural WFGY fixes with measurable acceptance targets.
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---
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## Quick routes to per-provider pages
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- Tesseract: [tesseract.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/DocumentAI_OCR/tesseract.md)
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- Google Document AI: [google_docai.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/DocumentAI_OCR/google_docai.md)
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- AWS Textract: [aws_textract.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/DocumentAI_OCR/aws_textract.md)
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- Azure OCR: [azure_ocr.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/DocumentAI_OCR/azure_ocr.md)
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- ABBYY: [abbyy.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/DocumentAI_OCR/abbyy.md)
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- PaddleOCR: [paddleocr.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/DocumentAI_OCR/paddleocr.md)
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## 📌 When to use this folder
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Use this map if you see any of these problems:
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- OCR extracts text but loses **tables or column alignment**.
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- Words are captured but **semantic grouping is wrong** (paragraphs broken).
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- Citations don’t match the **original scanned page**.
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- Layout-aware models drift after **format changes** (e.g. headers, forms).
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- Two-column PDFs or rotated scans break retrieval.
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- Cloud OCR services return **different JSON fields** each run.
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---
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## When to use this folder
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- OCR extracts text but misses table alignment or field boundaries.
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- High word recall but wrong semantic grouping.
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- Citations mismatch scanned sections.
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- Layout-aware models drift when format changes.
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- Two-column or rotated pages break retrieval.
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- Cloud OCR service gives inconsistent JSON schema across runs.
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## 🎯 Acceptance targets for OCR systems
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Think of these as “green lights” after your OCR step:
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- **ΔS(question, extracted text) ≤ 0.45** (semantic match stays tight).
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- **Coverage ≥ 0.70** of target section or table.
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- **λ stays convergent** across 3 paraphrases and 2 random seeds.
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- **E_resonance stays flat** across long documents (no drifting answers).
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---
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## Acceptance targets for any OCR system
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## 🚀 Quick routes — per-provider guides
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- ΔS(question, extracted text) ≤ 0.45
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- Field/section coverage ≥ 0.70
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- λ remains convergent across 3 paraphrases and 2 seeds
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- E_resonance flat over long document windows
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| Provider / Tool | Open this guide |
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|-------------------------|-----------------|
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| **Tesseract** (open-source OCR) | [tesseract.md](./tesseract.md) |
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| **Google Document AI** | [google_docai.md](./google_docai.md) |
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| **AWS Textract** | [aws_textract.md](./aws_textract.md) |
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| **Azure OCR** | [azure_ocr.md](./azure_ocr.md) |
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| **ABBYY** (enterprise OCR) | [abbyy.md](./abbyy.md) |
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| **PaddleOCR** (open-source) | [paddleocr.md](./paddleocr.md) |
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---
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## Map symptoms → structural fixes (Problem Map)
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## 🛠️ Common symptoms → exact fixes
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- **High similarity but wrong snippet**
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→ [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- **Traceability missing, citations don’t line up with scanned region**
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→ [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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→ [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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- **Chunking instability (multi-column / rotated scans)**
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→ [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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- **Cold boot / wrong version OCR model**
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→ [bootstrap-ordering.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md)
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→ [predeploy-collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md)
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- **Hybrid OCR (vision + text) worse than single mode**
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→ [pattern_query_parsing_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md)
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| Symptom | Likely cause | Fix page |
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|---------|--------------|----------|
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| High similarity but wrong snippet | Embeddings confuse words with meaning | [embedding-vs-semantic.md](../../embedding-vs-semantic.md) |
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| Citations don’t line up with scanned region | Missing traceability or weak schema | [retrieval-traceability.md](../../retrieval-traceability.md) · [data-contracts.md](../../data-contracts.md) |
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| Multi-column / rotated pages fail | Chunking instability | [chunking-checklist.md](../../chunking-checklist.md) |
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| Wrong OCR version after deploy | Boot ordering or pre-deploy collapse | [bootstrap-ordering.md](../../bootstrap-ordering.md) · [predeploy-collapse.md](../../predeploy-collapse.md) |
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| OCR+Vision hybrid worse than single | Query parsing split issue | [pattern_query_parsing_split.md](../../patterns/pattern_query_parsing_split.md) |
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---
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## 60-second fix checklist
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## ✅ 60-second fix checklist
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1. Run OCR twice (two providers or seeds) → compare ΔS & λ.
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2. Validate JSON schema → enforce `{page_id, bbox, text, confidence}`.
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3. De-rotate scans, split multi-column before embedding.
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4. Confirm **coverage ≥ 0.70** on a gold page.
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5. Force “cite then explain” in downstream reasoning steps.
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---
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## ❓ FAQ (beginner-friendly)
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**Q: What is ΔS and why should I care?**
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ΔS measures semantic drift — if it’s above 0.45, your OCR text no longer matches the question well. Keep it lower to ensure stable answers.
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**Q: What does λ mean in practice?**
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λ checks consistency across paraphrases. If the system gives different answers for re-phrased questions, λ is unstable.
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**Q: Why do my citations not match the scanned PDF?**
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Usually because the OCR JSON has no stable IDs or coordinates. Fix by enforcing traceability fields like `page_id` and `bbox`.
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**Q: My OCR works on simple PDFs but fails on forms or invoices. Why?**
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That’s a **chunking issue**. Multi-column and rotated layouts need pre-processing before feeding to embeddings.
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**Q: Do I need to switch providers if accuracy is low?**
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Not always. Most errors come from pipeline design (chunking, contracts, retrieval) rather than the OCR engine itself.
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1. Run OCR twice with different seeds / providers. Compare ΔS and λ.
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2. Validate JSON schema consistency: enforce fields `{page_id, bbox, text, confidence}`.
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3. Apply de-rotation and multi-column split before embedding.
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4. Check coverage ≥ 0.70 on a gold page.
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5. Enforce cite-then-explain in downstream reasoning.
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---
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