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Extract Tables from PDF

Not just bank statements — any table, in any PDF, extracted by meaning.

Most “PDF table extraction” tools are built and tuned for one shape of table — a bank statement, say — and fall apart on anything else: a shipping manifest, a lab report, a price list, a government filing with an unusual column layout. Convertitive Extract handles the common financial document types (bank statements, invoices, receipts) with dedicated extraction logic tuned to each one, and falls back to a generic table mode for everything else — any PDF page that is clearly tabular but doesn’t match those specific shapes. In every mode the approach is the same: identify columns by what the data means, not by where ruled lines happen to fall on the page, so merged cells, unruled columns, and multi-line entries don’t corrupt the output the way they do with geometry-based converters. If a table has no header row, columns are labeled column_1, column_2, and so on rather than forcing a guess.

./extract

Extract any table from any PDF

Upload a document. Preview the extracted table free, sign in, then export to Excel or CSV.

How to use

  1. Upload your PDF

    Drop in any PDF containing a table — financial document or otherwise, scanned or digital.

  2. Automatic classification

    Extract first classifies the page (bank statement, invoice, receipt, or generic table) so it applies the right extraction logic, then reads the table by column meaning.

  3. Preview free

    Review the detected columns and rows on-screen before exporting anything — no account needed to look.

  4. Sign in and export

    Sign in (free) to remove the preview watermark, then download a typed XLSX or CSV file.

Frequently asked questions

Does this only work on bank statements and invoices?
No. Bank statements, invoices, and receipts get dedicated extraction logic because their column structures are well-known, but any other tabular PDF — a shipping manifest, a price list, a report table — is handled by a generic table mode that infers column headers from the page (or labels them column_1, column_2, etc. if there's no header row) and preserves the table's actual shape rather than forcing it into a financial template.
How is this different from a ruled-line table detector?
Ruled-line detectors slice a page into cells based on where lines are drawn, which breaks the moment a cell wraps across two lines, a column has no visible ruling, or a header repeats across pages. Extract's model is prompted to identify column meaning instead — this text is a date, this block is one description regardless of the line breaks in it — so it stays correct on tables that don't fit a clean grid.
What happens with a table that has no header row?
Generic-table mode labels columns column_1, column_2, and so on rather than inventing header names it can't verify, so you always know which values in the output are inferred versus directly read from the page.
Can it extract multiple tables from a multi-page document?
Yes. Pages are processed and then stitched together into one table, with repeated header rows (common across multi-page reports) dropped so you don't get the header duplicated as a data row on every page.
Does it work on scanned or photographed pages?
Yes. The page is read as an image regardless of whether it's a native digital PDF or a scan, with a flag set when a page appears scanned so you know to double-check anything ambiguous, like an OCR-confusable digit.
Is trying it free?
Yes. Uploading a document and previewing the extracted table on-screen requires no account. A free sign-in is only needed to download the watermark-free Excel or CSV file.

About

Classification before extraction

Before extracting anything, Extract runs a lightweight classification pass on each page: is this a bank statement, invoice, receipt, or a generic table, and does it look scanned. That classification picks which extraction prompt runs next, so a bank statement gets bank-statement-tuned column targets (date, description, debit, credit, balance) while an unrelated table gets the generic-table prompt, which explicitly avoids forcing data into a financial schema it doesn't fit.

Why 'any table' is a harder claim than it sounds

A tool tuned for one document type can look deceptively accurate in a demo and then fail silently on the next PDF a user throws at it — wrong document, wrong assumptions, garbage output with no warning. Supporting a genuine generic-table fallback, one that preserves whatever shape the source table actually has rather than mapping it onto a template, is what makes it reasonable to say "any table" instead of "any bank statement."

Sources & references

Authoritative references behind the math, constants, and tables on this page. Verified by Buğra Sözeri on the dates shown and re-checked at every deploy.