pdf-processing_skill

Overview

This skill extracts text and tables from PDF files, fills form fields, merges and splits documents, and converts pages to images for OCR. It relies on lightweight Python libraries to handle common PDF workflows reliably. Use it to automate document processing, data extraction, and batch transformations.

How this skill works

The skill uses pdfplumber for text and table extraction and pypdf for merging and splitting. For scanned or image-based PDFs it suggests converting pages with pdf2image and applying OCR with pytesseract. It includes patterns for reading all pages, extracting tables to CSV, filling form fields, and basic error handling and performance tips.

When to use it

• You need to extract structured or unstructured text from digital PDFs
• You must extract tables and export them to CSV or spreadsheets
• You need to merge multiple PDFs or split large documents into parts
• You must fill or validate PDF form fields programmatically
• You need OCR for scanned documents or to convert pages to images

Best practices

• Prefer pdfplumber for accurate text and table extraction from digital PDFs
• Process only required pages to reduce memory and CPU use
• Batch pages or use multiprocessing when handling many files
• Fall back to pdf2image + pytesseract for scanned pages with no extractable text
• Always close PDF objects and handle exceptions to avoid file corruption

Example use cases

• Extract all text from a multi-page contract and save to a text file for searching
• Detect and export invoice tables to CSV for financial ingestion
• Merge multiple reports into a single PDF for archival or distribution
• Split a large scanned document into chapter files and apply OCR to each page
• Automatically fill and validate PDF forms (names, dates, checkboxes) before distribution