for text and layout-aware extraction
tabula-py for table detection and extraction
Why two extraction libraries? Because PDFs are chaos. A PDF is fundamentally a visual format — it knows where to draw characters, not what a "table" is. pdfplumber is excellent at layout-aware text extraction, while tabula's table detection handles structured grids better. Using each for what it does best gives much more reliable output than forcing one library to do everything.
Why local-first matters
Most "free PDF converter" sites are upload services. That's fine for a recipe PDF — less fine for contracts, bank statements, or client data. This tool processes everything locally:
git clone https://github.com/TsvetanG2/PDF-To-Excel-Converter.git
cd pdf-to-excel-converter
pip install -r requirements.txt
python pdftoexcel.py
Then open http://localhost:5000, upload, convert, done. Your files never leave your machine.
Honest limitations
I'm not going to pretend this beats commercial tools on every PDF. Scanned documents (images of text) need OCR, which isn't in scope here — this works on PDFs with an actual text layer. And table detection on documents with creative, merged-cell layouts is a hard problem for every tool in this space, including this one. For typical reports, exports, and structured documents, it does the job well.
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