PDF Table Extraction with AI: From Messy Tables to Clean CSV
PDF table extraction is the hardest parsing problem. We compared table-transformer, MinerU and LLM-based methods with real accuracy notes.
💡 What You Will Learn
PDF table extraction is the hardest parsing problem. We compared table-transformer, MinerU and LLM-based methods with real accuracy notes.
Every RAG engineer has a PDF table horror story: a financial report where the numbers ended up in the wrong columns. PDF table extraction is genuinely the hardest part of document parsing, because tables have no natural reading order.
Three Approaches That Work
Table Transformer (microsoft/table-transformer, 2,935 stars) is a specialized vision model that detects table regions and cells - the classic baseline and still the most reliable for bordered tables. MinerU (76,942 stars) includes table structure recognition in its pipeline and outputs tables as markdown or HTML in one pass. LLM-in-the-loop is the best fallback for borderless and rotated tables: feed parsed or OCR text with a strict JSON schema.
Practical recipe: run MinerU, spot-check 10 percent of tables, pass broken ones to an LLM with a column schema, then validate with sum checks on known columns - financial tables nearly always have a total row, so summing the extracted column and comparing to the total catches column shifts instantly.
Comparison
| Tool | Strength | Stars |
|---|---|---|
| Table Transformer | Bordered table detection | 2,935 |
| MinerU | All-in-one parsing | 76,942 |
| LLM in the loop | Messy tables fallback | - |
FAQ
Q: Can I extract tables from scanned PDFs?
A: Yes - run OCR first (MinerU does this internally), then table recognition on the OCR output.
Q: Which is better, detection models or LLMs?
A: Detection models win on speed and structure fidelity; LLMs win on messy, borderless, or rotated tables. Use both in sequence.
