AI Answer Library
Short answer
Yes, but only if you handle each document type separately — dumping everything into one pipeline does not work. Scans must go through OCR first, and complex layouts additionally need layout analysis to recover the correct reading order. Tables with merged cells or multi-level headers will almost certainly be mangled by ordinary text chunking; they need to be extracted as structured data before ingestion. The real cost sits in the preprocessing pipeline and human spot-checks, not in the model. When the source documents are poor, cleaning them up beats swapping models.
The table is organised around ingestion into an enterprise knowledge base. The difficulty column is relative, not absolute — a 300 dpi contract scan and a skewed phone photo of the same page are worlds apart. In practice, count what proportion of your corpus falls into each type, then do the top one or two properly instead of chasing full coverage from day one.
| Document type | Main difficulty | What works | Effort |
|---|---|---|---|
| Digital PDF, Word, web pages | Recognition is a non-issue; chunk granularity is the question | Chunk along heading hierarchy and keep the section path as metadata | Low — off-the-shelf tooling suffices |
| Clean scans (300 dpi and up, printed text) | Character accuracy is fine; reading order must be reconstructed | OCR plus layout analysis, with columns, headers and footers handled separately | Medium — needs sampled correction |
| Poor scans (handwriting, stamps, skewed photos) | High and unstable error rate, failing unpredictably | Extract key fields only, force human review, and do not ingest full text | High — manual entry is often cheaper |
| Complex tables (merged cells, multi-level headers, page breaks) | Flattening to text destroys row-column mapping and misaligns every number | Run dedicated table extraction into structured storage, and answer by querying rather than retrieving | High, and every table needs verification |
| Drawings, flowcharts, diagrams | Meaning lives in the graphical relationships and is mostly lost in text | Have a multimodal model produce a structured description for ingestion while keeping the original for human inspection | High, with accuracy resting on human confirmation |
Ordinary text chunking assumes adjacent characters belong to the same sentence, but a table's meaning is two-dimensional: a number means what it means because of both its row label and its column label. Flatten it into lines and that mapping is gone. The model then sees isolated numbers, guesses labels from whatever words are nearest, and confidently reports the South China figure when asked about East China in Q3 2025. Continuation tables are worse: the second page usually carries no header, so after flattening there is no way to tell which column a number belongs to. The workable approach is to extract tables separately into structured storage — a database table, or JSON carrying the full header — and answer with a structured query instead of vector retrieval. The fallback is to generate a natural-language summary per table with the context spelled out, trading precision for coverage.
Once the pipeline has run, sample a batch of documents and put the original page next to the parsed output for a business colleague to mark up. This step gets skipped because it looks unglamorous, yet it is the only way systematic errors surface — every stamped contract losing a digit from the amount, every continuation table attributed to the wrong parent. Stratify the sample by document type with a few dozen per type rather than sampling uniformly from the whole corpus, or the small-but-critical categories will never be drawn. Classify each error found: recognition, ordering, or chunking? The three have completely different fixes. Finally, freeze the annotated batch into a regression set and replay it after every pipeline change, otherwise you cannot tell whether this change fixed one problem while breaking two others.
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