# Document AI & OCR — Global Fix Map

🏥 Quick Return to Emergency Room

> You are in a specialist desk. > For full triage and doctors on duty, return here: > > - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/README.md) > - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md) > > Think of this page as a sub-room. > If you want full consultation and prescriptions, go back to the Emergency Room lobby.

A **beginner-friendly hub** to stabilize OCR (Optical Character Recognition) and document AI pipelines across providers and open-source stacks. This page helps you: 1. Understand common OCR failures. 2. Jump directly to per-tool guides. 3. Apply structural WFGY fixes with measurable acceptance targets. --- ## 📌 When to use this folder Use this map if you see any of these problems: - OCR extracts text but loses **tables or column alignment**. - Words are captured but **semantic grouping is wrong** (paragraphs broken). - Citations don’t match the **original scanned page**. - Layout-aware models drift after **format changes** (e.g. headers, forms). - Two-column PDFs or rotated scans break retrieval. - Cloud OCR services return **different JSON fields** each run. --- ## 🎯 Acceptance targets for OCR systems Think of these as “green lights” after your OCR step: - **ΔS(question, extracted text) ≤ 0.45** (semantic match stays tight). - **Coverage ≥ 0.70** of target section or table. - **λ stays convergent** across 3 paraphrases and 2 random seeds. - **E_resonance stays flat** across long documents (no drifting answers). --- ## 🚀 Quick routes — per-provider guides | Provider / Tool | Open this guide | |-------------------------|-----------------| | **Tesseract** (open-source OCR) | [tesseract.md](./tesseract.md) | | **Google Document AI** | [google_docai.md](./google_docai.md) | | **AWS Textract** | [aws_textract.md](./aws_textract.md) | | **Azure OCR** | [azure_ocr.md](./azure_ocr.md) | | **ABBYY** (enterprise OCR) | [abbyy.md](./abbyy.md) | | **PaddleOCR** (open-source) | [paddleocr.md](./paddleocr.md) | --- ## 🛠️ Common symptoms → exact fixes | Symptom | Likely cause | Fix page | |---------|--------------|----------| | High similarity but wrong snippet | Embeddings confuse words with meaning | [embedding-vs-semantic.md](../../embedding-vs-semantic.md) | | 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) | | Multi-column / rotated pages fail | Chunking instability | [chunking-checklist.md](../../chunking-checklist.md) | | Wrong OCR version after deploy | Boot ordering or pre-deploy collapse | [bootstrap-ordering.md](../../bootstrap-ordering.md) · [predeploy-collapse.md](../../predeploy-collapse.md) | | OCR+Vision hybrid worse than single | Query parsing split issue | [pattern_query_parsing_split.md](../../patterns/pattern_query_parsing_split.md) | --- ## ✅ 60-second fix checklist 1. Run OCR twice (two providers or seeds) → compare ΔS & λ. 2. Validate JSON schema → enforce `{page_id, bbox, text, confidence}`. 3. De-rotate scans, split multi-column before embedding. 4. Confirm **coverage ≥ 0.70** on a gold page. 5. Force “cite then explain” in downstream reasoning steps. --- ## ❓ FAQ (beginner-friendly) **Q: What is ΔS and why should I care?** 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. **Q: What does λ mean in practice?** λ checks consistency across paraphrases. If the system gives different answers for re-phrased questions, λ is unstable. **Q: Why do my citations not match the scanned PDF?** Usually because the OCR JSON has no stable IDs or coordinates. Fix by enforcing traceability fields like `page_id` and `bbox`. **Q: My OCR works on simple PDFs but fails on forms or invoices. Why?** That’s a **chunking issue**. Multi-column and rotated layouts need pre-processing before feeding to embeddings. **Q: Do I need to switch providers if accuracy is low?** Not always. Most errors come from pipeline design (chunking, contracts, retrieval) rather than the OCR engine itself. --- ### 🔗 Quick-Start Downloads (60 sec) | Tool | Link | 3-Step Setup | |------|------|--------------| | **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/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](/recognition/README.md) | External citations, integrations, and ecosystem proof | | ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) | | ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems | | ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) | | 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map | | 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis | | 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map | | 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap | | 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS | | 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control | | 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users | If this repository helped, starring it improves discovery so more builders can find the docs and tools. [](https://github.com/onestardao/WFGY)