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WFGY/ProblemMap/GlobalFixMap/DocumentAI_OCR/tesseract.md

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Tesseract OCR: Guardrails and Fix Patterns

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A compact field guide to stabilize Tesseract or Tesseract.js when used in AI pipelines, document ingestion, or hybrid RAG flows. Use these checks to pin down the failure, then jump directly to the WFGY structural fixes.

Open these first

Core acceptance

  • ΔS(ground truth, OCR text) ≤ 0.35
  • Coverage ≥ 0.85 tokens per line
  • λ stays convergent across three OCR runs
  • Table cell alignment error ≤ 1 cell
  • Unicode normalization accuracy ≥ 0.95

Typical Tesseract breakpoints → exact fix

Symptom Likely cause Open this
Garbled characters (utf-8 vs utf-16) codepage drift or bad normalization Chunking Checklist, Data Contracts
Wrong line breaks, merged words bounding box drift or missing language model Retrieval Traceability
High similarity but meaningless embeddings dirty OCR tokens, confusable glyphs Embedding ≠ Semantic
First call returns empty result engine not ready, fonts not loaded Bootstrap Ordering
Index ingestion with half-baked OCR text deployment race or auth loop Deployment Deadlock, Pre-Deploy Collapse

Fix in 60 seconds

  1. Run three OCR passes on the same page.
    Compare λ states. If they diverge, normalize with Unicode NFC and re-chunk.

  2. Enforce contracts.
    Require {line_id, bbox, text, lang} per line. Reject entries missing lang.

  3. ΔS probe.
    Compute ΔS against ground-truth anchors (gold set). If ΔS ≥ 0.45, enforce schema locks and rerun chunk alignment.

  4. Publish only after stable run.
    Coverage ≥ 0.85 and ΔS ≤ 0.35 across 3 seeds.

Copy-paste prompt for OCR → LLM stage

You have TXTOS and the WFGY Problem Map loaded.

My OCR pipeline used Tesseract and produced N lines with fields {line_id, bbox, text, lang}.
Question: "{user_question}"

Do:

1. Validate ΔS against the anchor set.
2. If ΔS ≥ 0.45, point me to the minimal fix page (chunking-checklist, embedding-vs-semantic, retrieval-traceability).
3. Return JSON:
   { "citations": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "...", "next_fix": "..." }

Common gotchas

  • Mixed fonts break recognition. Always load the correct traineddata file.
  • Parallel OCR threads overwrite the same KV entry. Use idempotency keys.
  • Tesseract.js on web workers drops unicode range ≥ U+3000. Force full model load.
  • Line segmentation differs across seeds. Lock page segmentation mode (PSM).

🔗 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 + ”
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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