pdf

Text and table extraction from PDFs uses libraries like pdfplumber and pypdf to parse document content programmatically. For scanned documents, pytesseract with pdf2image enables OCR-based extraction, converting images to searchable text and structured table data.

Yes, fillable forms can be completed using pypdf and reportlab to write field values directly. Non-fillable forms require visual analysis and annotation—the Skill uses bounding-box validation and image conversion to locate and populate fields programmatically.

Batch PDF processing combines pypdf or pdfplumber for parsing, pytesseract for OCR on scanned documents, and reportlab for output generation. Ready-to-run scripts handle field extraction, metadata retrieval, and document manipulation across multiple files in a single workflow.

Document manipulation—merging, splitting, rotating, watermarking, and password-protecting PDFs—is handled by pypdf and qpdf. These tools operate on page-level structures, allowing precise control over document assembly and transformation without manual intervention.

Scanned PDFs containing only images require OCR to extract text. pytesseract with pdf2image converts PDF pages to images, applies optical character recognition, and returns structured text suitable for parsing and data extraction workflows.

Password-protected PDFs can be handled with qpdf decryption support. Complex layouts with mixed text, images, and tables may require combining pdfplumber for precision and pytesseract for scanned regions. Encrypted forms may limit programmatic field access depending on permission flags.