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180 lines
9.1 KiB
Markdown
180 lines
9.1 KiB
Markdown
# Tokenization & Casing — OCR Parsing Guardrails
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **OCR_Parsing**.
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> To reorient, go back here:
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>
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> - [**OCR_Parsing** — text recognition and document structure parsing](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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A focused fix page for post-OCR text where casing, spaces, or token boundaries are corrupted. Use this to normalize the stream **before** chunking/embedding, and verify with measurable targets. Works across Tesseract, Google DocAI, Azure OCR, ABBYY, PaddleOCR, and custom engines.
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## When to use this page
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- Words are split or glued (e.g., `re tri eval`, `metadataindex`).
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- Case flaps mid-sentence (`tHE DocUment`), acronyms collapse (`R a G`).
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- Invisible characters or double spaces change token counts.
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- Chunkers behave inconsistently between runs with the same image/PDF.
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- Embedding recall looks fine locally but retrieval ΔS stays high.
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## Open these first
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- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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- Chunking checklist: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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- Retrieval traceability (cite-then-explain schema): [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Payload schema fences: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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- Embedding vs meaning (when token noise leaks into vectors): [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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## Acceptance targets
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- ΔS(question, retrieved) ≤ 0.45 after normalization.
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- Coverage to target section ≥ 0.70 on three paraphrases.
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- λ remains convergent across two seeds.
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- Token count variance for the same page ≤ 1 percent after normalization.
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---
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## Symptoms → exact fix
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- **Mid-token splits or merges** (`infor mation`, `vectorstoreindex`)
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Fix No.1: **Rejoin by dictionary and layout anchors.**
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Use wordlist + n-gram agree check, prefer joins that reduce ΔS on a small gold set.
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See: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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- **Casing drift** (random upper/lower), acronym scatter (`r.a.g.`)
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Fix No.2: **Casing normalization with protected spans.**
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Protect enums, acronyms, chemical names, LaTeX blocks, code fonts.
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- **Whitespace noise** (NBSP, thin space, double space)
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Fix No.3: **Unicode normalization + space collapse**, keep offsets table.
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Record before/after offset map to preserve citation alignment.
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See: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- **Punctuation misreads** (`l` vs `1`, `O` vs `0`, `,` vs `.`)
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Fix No.4: **Confusable set pass + local language model vote.**
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Only apply inside numeric or acronym contexts, keep audit log.
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- **Tokenizer mismatch across components**
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Fix No.5: **Single tokenizer contract for parse → chunk → embed.**
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Declare `tokenizer_name`, `lowercase`, `strip_accents` in payload.
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See: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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---
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## 60-second fix checklist
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1) **Normalize**
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- Unicode NFC, strip BOM, collapse spaces except inside code/math blocks.
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- Replace NBSP and thin spaces with ASCII space.
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- Build `offset_map` old→new for citations.
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2) **Protect**
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- Detect protected spans: URLs, emails, hex, code, LaTeX, table headers, known acronyms.
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- Freeze casing and punctuation inside protected spans.
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3) **Rejoin / Split**
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- Rejoin candidates by dictionary + bigram score.
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- Split stuck words when edit distance to dictionary is lower after split.
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4) **Contract**
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- Emit `tokenizer_contract.json`:
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`{ "tokenizer": "bert-base-uncased", "lowercase": true, "strip_accents": true }`
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Attach to every downstream step.
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See: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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5) **Verify**
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- Recompute ΔS on a 20-question gold set.
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- If ΔS stays ≥ 0.60, open [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) and rebuild index with the same tokenizer contract.
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---
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## Minimal pipeline patch (pseudo)
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```python
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text = ocr_text
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text = unicode_normalize_nfc(text)
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text, offset_map = collapse_spaces_with_offsets(text)
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spans = detect_protected_spans(text) # urls, code, latex, acronyms
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text = normalize_casing(text, protect=spans)
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cands = find_split_merge_candidates(text, protect=spans)
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text = apply_split_merge(text, cands, scorer="bigram+dict")
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emit_payload(
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content=text,
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offset_map=offset_map,
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tokenizer_contract={
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"tokenizer": "bert-base-uncased",
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"lowercase": True,
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"strip_accents": True
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}
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)
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````
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Keep the offset map, or citations will drift.
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Tracing and schema rules come from: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) · [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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---
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## Eval recipe
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* Build a tiny page-level gold: 10–20 questions with expected anchor sections.
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* Measure before/after: ΔS(question, retrieved), coverage, λ states.
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* Acceptance to sign off: ΔS ≤ 0.45, coverage ≥ 0.70, λ convergent on both seeds.
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* Log token count and confusable corrections per page for audit.
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---
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## When to escalate
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* ΔS remains high after normalization and re-chunking
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Open: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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* Citations drift after formatting changes
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Open: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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* Layout destroys sentence flow or table cells
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Open: **OCR\_Parsing/table\_parsing.md** once added, and the general [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **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 + \<your question>” |
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| **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 |
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---
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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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