Extract Tables from PDF for Analysis
Pull structured rows and columns out of financial statements, research papers, invoices, and scanned reports — ready to analyse in Excel, drop into a pandas DataFrame, or push to a database. Mark what you want, skip what you don't, and keep everything in your browser.
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Drop your PDF or image here, or click to browse
PDF · JPG · PNG · Up to 50 MB · Processed 100% in your browser
How to Extract Tabular Data from a PDF
Built for the workflow: open report → grab the tables you need → push to the next step (Excel, Python, BI tool, ETL job). No paid API, no account, no rate limits.
Load the Report
Drop a PDF (or a scanned image). Browse page thumbnails on the left to find the tables you care about.
Auto-Detect or Draw
One-click auto-detect proposes rectangles around tabular regions. Keep what works, delete what doesn’t, and draw anything it missed.
Extract + Review
Tables that continue across pages are merged automatically. The preview grid sits next to a crop of the source so you can verify fast.
Export for the Next Step
Multi-sheet .xlsx for Excel work, or per-table CSV for importing into Python, R, or a database pipeline.
Who Uses This
Typical workflows where a browser-native table extractor saves hours vs. retyping or waiting on an API service.
Financial analysts
Pull tables out of annual reports, 10-Ks, earnings releases and bank statements — then drop straight into models or BI dashboards. Auto-detect handles most structured reports; manual rectangles catch the awkward ones.
Researchers
Extract data tables from published papers for meta-analysis or reproduction work. Cross-page merging means a table split across a page break comes out as one continuous dataset.
Ops & accounting
Turn invoices, receipts, and supplier statements into CSV ready for your reconciliation workflow. Privacy matters: customer names and financial figures never hit a third-party server.
Why “No Upload” Matters for Data Extraction
The documents most worth extracting data from — internal financials, clinical studies, supplier contracts — are also the documents most sensitive to leak. Most online extractors send them to a server, often with opaque retention policies. This one doesn't. pdf.js parses the document, Tesseract.js runs OCR when needed, and SheetJS writes the .xlsx — all inside your browser tab.
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