pdf

To extract text and tables from PDF documents, you can use Python libraries like pypdf and pdfplumber to parse binary streams into structured data. This skill automates that process, pulling text, tables, and images from PDF files for administrative and reporting workflows.

Yes, you can merge, split, rotate, and edit PDFs programmatically using Python libraries such as pypdf and reportlab. This skill centralizes these document operations into a single workflow, allowing you to transform and consolidate PDF archives without manual steps.

Yes, this skill applies OCR for scanned PDF files alongside pdfplumber and pypdf. It handles font embedding and non-Latin text extraction, allowing you to process and extract data from scanned documents that standard text extraction cannot parse.

The best way to automate invoice processing from large sets of PDFs is using a Python skill that extracts totals, dates, and vendor names into structured data. This tool uses pypdf and pdfplumber to parse binary streams and consolidate invoice data automatically.

You can generate new PDFs and fill forms using reportlab and pypdf within this skill. It handles font embedding and non-Latin text while creating new documents or filling existing forms, satisfying requirements for automated report generation and archival workflows.

Yes, you can encrypt and decrypt PDF files using pypdf as part of this skill's workflow. It handles document security alongside merging, splitting, and editing operations, allowing you to secure sensitive PDF data during archival and consolidation processes.