# Tables and Columns: OCR Parsing Guardrails
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> You are in a sub-page of **OCR_Parsing**. > To reorient, go back here: > > - [**OCR_Parsing** — text recognition and document structure parsing](./README.md) > - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md) > - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md) > > Think of this page as a desk within a ward. > If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/ocr-parsing-checklist.md) - Snippet and citation schema: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) - Why this snippet: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) - Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) - Chunking checklist: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) - **Numeric columns treated as text** Normalize units and numeric types before embedding. See: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) --- ## Fix in 60 seconds 1) **Extract layout objects** Ensure the OCR output includes `page`, `block`, `bbox`, `table`, `row`, `cell`, `col_idx`, `row_idx`. 2) **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)`. 3) **Lock a table schema** Contract fields: `table_id, row_id, col_id, header_norm, value_norm, page, bbox, units, type_num|type_text`. 4) **Chunk by row or record** Prefer one row per chunk, include the header set as structured metadata. 5) **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 ``, ``, `` 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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](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. [![GitHub Repo stars](https://img.shields.io/github/stars/onestardao/WFGY?style=social)](https://github.com/onestardao/WFGY) 要我接著排第三頁嗎？依序我會做：**`layout_headers_and_footers.md`**。