pdf

PDF text and table extraction uses pdfplumber and pypdf to parse document files and pull clean text, tables, metadata, and images. It handles coordinate-aware parsing to return structured, usable data from both native and scanned documents.

Filling non-fillable PDFs is supported by estimating coordinates and placing annotations accurately. When fillable fields are available, the workflow detects them automatically; otherwise, it calculates positions using pdf2image and Pillow to render and validate placements.

OCR for scanned PDF documents is handled by rendering pages into images with pdf2image and Pillow. The process converts visual document content into searchable text, enabling text extraction from image-based or scanned files that lack embedded text layers.

Merging, splitting, rotating, and watermarking PDF files are core document transformation tasks supported by pypdf. It manipulates document structures directly to combine pages, extract specific sections, or apply overlays, generating modified PDF outputs.

Yes, pypdf and pdfplumber are required dependencies for PDF automation, along with pdf2image and Pillow. They provide the foundational libraries for text extraction, coordinate-aware parsing, rendering, and validation across common document processing tasks.

Extracting images and metadata from PDF files uses pdfplumber and pypdf to parse document assets and properties. The process isolates visual elements and document attributes, returning them alongside extracted text and tables for comprehensive data retrieval.