Models in this group take whole documents as input: PDFs, scanned pages and office files. Each specification table gives three hardware tiers — Minimum, the smallest setup on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is the number of sample inputs processed per hour on that hardware. The sample input here is one A4 page scanned at 300 dpi carrying a single table. Use these rates for initial sizing. Before production, benchmark your own data to validate accuracy, latency, throughput and cost.
Vendor: Microsoft
What it does: locates each table on a page and reconstructs its grid of rows, columns and merged cells. Use it for tables with printed ruling lines — financial statements, regulatory returns, printed reports — at high volume. It returns the grid only, so it is paired with an OCR step that reads the text inside each cell.
PP-StructureV3
Vendor: Baidu (PaddlePaddle)
What it does: page layout, table structure and character reading in a single pass, so one run returns both the tables and the paragraphs around them. Accuracy is strongest on English and Chinese text. A sound default when the whole page needs converting, not just the tables.
Vendor: IBM
What it does: converts an entire document into structured output and recovers tables whose headers are merged or stacked several rows deep. Suited to financial and scientific reports, where a figure means nothing unless the header above it is attached correctly.
Surya
Vendor: Datalab
What it does: detects layout, works out the order in which blocks should be read, reads the characters and recovers table structure, in roughly ninety languages. A practical choice for multilingual archives and for scans of uneven quality.
MinerU
Vendor: OpenDataLab (Shanghai AI Laboratory)
What it does: converts PDFs in bulk while keeping tables, mathematical formulas and figures intact. Built for one-off jobs that turn a large document archive into machine-readable data.
Camelot / pdfplumber
Vendor: community-maintained open-source projects
What it does: reads the ruling lines and text positions already stored inside a PDF that was produced by software rather than scanned, and rebuilds the table from them. No neural model and no GPU are involved, so results are exact and repeatable — but it cannot read scans or photographs, which carry no text layer.
Azure Document Intelligence
Vendor: Microsoft
What it does: returns tables, labelled field values (such as an invoice number next to its label) and ticked checkboxes from a page. It ships as a container that runs inside your own cluster, so documents never leave your network.
Vendor: Amazon Web Services
What it does: analyses tables and forms, and accepts long documents as background jobs that report back when finished rather than making the caller wait. Usually chosen when the rest of the pipeline already runs on Amazon Web Services.
Google Document AI
Vendor: Google Cloud
What it does: reads page layout and forms and returns the tables found. It can also be trained on a document type you receive repeatedly — one supplier’s invoice, one agency’s form — to pull named fields out of it reliably.
Mistral OCR
Vendor: Mistral AI
What it does: reads a document and returns it as Markdown with the tables intact, coping with handwriting and dense multi-column layouts that defeat simpler extractors.