pdf

Text extraction from PDF files uses the pypdf and pdfplumber libraries to parse document content programmatically. It effortlessly pulls text content from existing PDFs, allowing you to automate data extraction from reports, research papers, and administrative documents.

PDF generation and merging are handled using the reportlab and pypdf libraries to create new documents and combine multiple files programmatically. You can generate new PDFs, merge multiple documents, split large files, and rotate pages to automate document processing workflows.

Text extraction from scanned PDFs uses pytesseract and pdf2image to apply OCR capabilities. These dependencies convert image-based document pages into searchable text, extending data extraction beyond standard text-based PDF files.

Splitting large PDF files and handling forms are supported through the pypdf dependency for document manipulation. You can divide large documents into smaller files and process interactive form fields programmatically for administrative tasks.

Table extraction fails on misaligned rows when the pdfplumber library cannot detect clear bounding boxes for structured data. Complex PDF layouts with merged cells or irregular column boundaries limit accurate data extraction from the document.