pdf

To extract text from scanned PDF documents, the skill automatically triggers OCR using pytesseract and Pillow. It detects missing text layers and applies optical character recognition to retrieve inaccessible content from image-only files.

The best way to extract complex tables and multi-column layouts from a PDF is using pdfplumber integration. This ensures high-fidelity data retrieval and layout preservation, preventing the structural mangling that standard extraction tools often cause.

You can merge, split, rotate, and populate form fields across various PDF architectures. The skill provides a robust toolkit for complex document manipulation, including watermarking pages and filling interactive forms natively.

PyMuPDF works well for high-fidelity PDF data extraction by ensuring accurate text retrieval and layout preservation. It integrates seamlessly with the skill to handle complex document structures and multi-column formatting robustly.

Standard PDF extraction fails on non-structured tables and scanned documents because they lack accessible text layers and have complex layouts. The skill overcomes this by combining intelligent extraction with OCR to process inaccessible content.