When documents become text, what breaks usually doesn't throw an error: multi-column paragraphs joined in the wrong order, merged table cells flattened, headers and footers mixed into the body, OCR typos in scans, images and strikethroughs gone. High OCR accuracy doesn't mean good RAG either: in one study, documents with strikethroughs and color marks scored 82.9% on OCR but only 52.8% on question answering. Chunking matters more than switching tools: from the same conversion output, chunking by heading hierarchy with the heading path attached raised table questions from 66.6% to 99.8%. So always spot-check parsed output against the originals for these failure types.
document_parse gains an effort parameter (fast/standard/deep) unifying anydoc WASM, OCR.space, and Docling VLM into a single dial; document_chunk's fieldAware mode extracts field names from table headers and metadata to solve RAG attribute conflation; document_smart_parse now returns a confidence score so agents decide whether to upgrade.
Traditional document parsing runs a fixed pipeline regardless of input, but contracts, financial reports, and technical manuals each need different strategies. Agentic Parsing lets LLM agents observe a document and dynamically choose tools — AgenticOCR parses only the regions that matter (70%+ visual token savings), and ParseBench shows even the best method scores only 84.9% across 2,000 enterprise pages. No silver bullet.
CHURRO represents full-page text, layout, and metadata in HDML, unifies multilingual historical data for a page-level VLM, and connects extraction to HistoryGenie for searchable, conversational archives.
I tested 10 open-source PDF parsing tools on four scanned NTU graduate entrance exams. VLM-based tools—Firecrawl, MinerU 3.4, and Marker v2—overwhelmingly beat conventional OCR on formulas and code, but installation was the real barrier: MinerU's old package name creates dependency hell, Marker's first model download takes 10 minutes, and PaddleOCR needs a separate engine. In practice, use RapidOCR for screening and MinerU or Firecrawl for close inspection.
Scans and complex layouts leave you no choice but to infer structure with a model. But the technical gap between MinerU, Marker, and Docling is far smaller than the licensing gap — MinerU needs a separate license past $20M monthly revenue, Marker's model weights need payment past a funding threshold, and only Docling is cleanly MIT. Read the LICENSE before the benchmark.
The most common mistake in feeding documents to an LLM isn't picking the wrong tool — it's picking the wrong layer. Structure already in the file goes to the conversion layer (milliseconds); text without structure goes to extraction; only inferred structure needs parsing. anydoc's 4.7ms against Docling's 513.6ms is a 109× gap, and most people jump straight to the most expensive layer.
DeepSeek-OCR's paper is titled Contexts Optical Compression -- OCR is just the means; what it actually validates is that 'rendering text as images and feeding them to a VLM' achieves 10x compression at 97% accuracy. This is a qualitative shift for long-context LLM and RAG token costs.