PDF Toolkit — Extract, Merge, Split, Fill, and OCR PDF Files

The problem

PDF interaction often fails when standard libraries mishandle complex layouts, tables, or scanned content. Developers struggle with inconsistent text extraction, broken Unicode rendering, and the complexity of programmatic PDF creation.

What it does

• Extracts structured text and tabular data from documents into pandas DataFrames.
• Automates document operations including merging, splitting, rotating, and watermarking.
• Generates dynamic PDF reports from scratch using layout engines and markup.
• Performs OCR on scanned documents to convert images into searchable text.
• Manages document security through encryption, decryption, and password protection.

Frameworks & tools

Python (pypdf, pdfplumber, reportlab, pytesseract), CLI tools (qpdf, poppler-utils, pdftk).

Why this beats prompting it yourself

Programming PDF logic by hand often leads to encoding errors or failed table detection. This skill provides the specific syntax for layout-aware extraction and correctly handles delicate tasks like subscript rendering and OCR pipelines that generic prompts usually hallucinate.

Use cases

• Convert messy PDF invoices into clean Excel or CSV files.
• Batch process and watermark a directory of legal documents.
• Build automated reporting pipelines that generate multi-page branded PDFs.
• Remove passwords from restricted documents for automated ingestion.

Known limitations

ReportLab lacks native Unicode subscript/superscript support in default fonts. OCR requires external Tesseract and Poppler dependencies.