PDF-native extraction vs. vision models for document processing (pymupdf.io)

Google’s Gemini 3.0 has reignited interest in multimodal document AI, but vendors integrating VLMs report persistent errors on complex layouts, formatting (strikethroughs, fonts), and bounding boxes. The core issue: vision models treat PDFs as images—rendering pages to pixels and using huge neural nets to simultaneously OCR, detect layout, and interpret semantics—which is powerful but costly, lossy, and hard to correct. For born-digital PDFs this is inefficient: rendering destroys embedded text, font/decoration metadata, vector graphics, annotations and reading-order hints that are already explicit in the file. PDF-native extraction (exemplified by PyMuPDF-Layout) reads PDF internals instead of reconstructing them. That yields perfect text fidelity (including formatting), vector-aware table detection driven by a GNN plus vector analysis (reported 97% table-structure accuracy on complex finance docs), and far lower resource needs—CPU inference, ~1.8M parameters, sub-second processing—versus multi-billion-parameter VLMs on GPUs. Scanned pages are handled via selective OCR (Tesseract, with options for other engines), while handwritten or highly degraded scans may still favor vision models. The practical takeaway: for invoices, contracts, reports and other born-digital documents, PDF-native pipelines are faster, cheaper and more accurate; use vision models primarily when pixels are the only source of truth.