If you have ever tried to convert a PDF invoice to Excel in your browser and ended up with a garbled mess of numbers in the wrong cells, you are not alone. Browser-based PDF converters miss roughly 40% of real-world invoice tables. Here is why — and how server-side processing using Camelot fixes it.
A PDF file does not store tables. It stores text at X/Y coordinates on a page. When you open a PDF in a browser, JavaScript has to guess where rows and columns are based on text positions. For simple tables with visible borders, this works reasonably well. For borderless tables, multi-column layouts, or scanned documents, it fails — often silently producing output that looks correct until you actually check the numbers.
Camelot is a Python library designed specifically for PDF table extraction. It uses two methods: lattice mode (for tables with visible borders) and stream mode (for borderless tables). When FlipFiles Pro processes your PDF, it tries both methods and picks the best result. If both fail, it falls back to Tabula — a Java-based alternative. This three-method approach achieves 94%+ accuracy on standard invoice formats.
Scanned PDFs are images — there is no text to extract at all. FlipFiles Pro runs Tesseract OCR first to extract the text, then passes the result to the table extraction pipeline. Browser tools simply cannot do this.
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