Tables and Columns: OCR Parsing Guardrails

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OCR_Parsing — text recognition and document structure parsing

WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

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Stabilize table and multi-column layouts before chunking or embedding. Prevent row/column swaps, header duplication, and order drift so retrieval stays aligned with ground truth.

Open these first

OCR end-to-end checklist: ocr-parsing-checklist.md
Snippet and citation schema: data-contracts.md
Why this snippet: retrieval-traceability.md
Embedding vs meaning: embedding-vs-semantic.md
Chunking checklist: chunking-checklist.md

Acceptance targets

ΔS(question, retrieved) ≤ 0.45 on table questions
Row and column order invariant under paraphrase probes
Coverage ≥ 0.70 to the target rows or section
λ remains convergent across three paraphrases and two seeds

Typical failure signatures → exact fix

Two-column pages read left page then right page
Normalize reading order and reflow columns before chunking. See: ocr-parsing-checklist.md
Header row duplicated into every row
Deduplicate repeating headers and lock a table schema in the data contract. See: data-contracts.md
Row fragments interleaved across pages
Use table bounding boxes and row stitching with page+y ordering. Verify with trace probes. See: retrieval-traceability.md
Merged cells collapse to free text
Expand merged cells to explicit coordinates (row_id, col_span) and normalize headers. See: chunking-checklist.md
Numeric columns treated as text
Normalize units and numeric types before embedding. See: embedding-vs-semantic.md

Fix in 60 seconds

Extract layout objects
Ensure the OCR output includes page, block, bbox, table, row, cell, col_idx, row_idx.
Rebuild true order
For multi-column pages, reflow by columns then by top-to-bottom within each column. For tables, order by (page, table_id, row_idx, col_idx).
Lock a table schema
Contract fields:
table_id, row_id, col_id, header_norm, value_norm, page, bbox, units, type_num|type_text.
Chunk by row or record
Prefer one row per chunk, include the header set as structured metadata.
Probe ΔS and λ
Ask three paraphrases of the same table question. ΔS should drop ≤ 0.45 and λ should not flip after schema lock.

Minimal recipes by engine

Google Document AI
Use the Form or Layout parsers. Keep tableBoundedRegions, layout.boundingPoly, and detectedLanguages. Reconstruct (row, col) grid, expand merged cells with col_span and row_span. Then apply the data contract.
AWS Textract
Use AnalyzeDocument with TABLES and FORMS. Walk CELL relationships to build (row, col). Carry Geometry.BoundingBox into metadata. Normalize header rows and numeric types.
Azure OCR
Use Read with styles and spans, or Layout to capture tables. Reorder by column regions when the page contains multi-column text outside tables.
ABBYY
Export XML or JSON keeping <block>, <row>, <cell> coordinates. Expand merged cells; dedupe repeated headers by key similarity.
PaddleOCR
Use table mode to get cell grid; post-process with bbox sorting and header normalization.

Data contract for table snippets

Required fields in each snippet:


{
"table\_id": "...",
"row\_id": "...",
"col\_id": "...",
"header\_norm": \["Year","Revenue\_USD"],
"value\_norm": "1234567",
"units": "USD",
"type": "number",
"page": 12,
"bbox": \[x0,y0,x1,y1],
"source\_url": "...",
"section\_id": "appendix\_B"
}

Mandatory rule: cite then explain. Never answer from table text without including table_id and row_id.

Verification

Row pick test: ask for a specific cell by coordinates and by header name. Both must resolve to the same snippet id.
Order stability: shuffle prompt headers and re-ask. λ must remain convergent.
Numeric sanity: unit conversions should not change the winning row.

If ΔS stays flat and high across k values, suspect metric or index mismatch. Open: retrieval-playbook.md

Copy-paste prompt for the LLM step


You have TXT OS and the WFGY Problem Map loaded.

My question targets a table. I provide structured snippets with fields:
{table\_id,row\_id,col\_id,header\_norm,value\_norm,units,type,page,bbox,source\_url,section\_id}

Tasks:

1. Validate cite-then-explain with explicit {table\_id,row\_id,col\_id}.
2. If headers appear duplicated or rows interleaved, fail fast and return the minimal structural fix
   referencing: ocr-parsing-checklist, data-contracts, retrieval-traceability, chunking-checklist.
3. Return JSON:
   { "citations": \[...], "answer": "...", "λ\_state": "→|←|<>|×", "ΔS": 0.xx, "next\_fix": "..." }
   Keep it auditable and short.

🔗 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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →