Can this skill handle scanned image-based PDFs?

Yes, the skill includes support for OCR (Optical Character Recognition) using Tesseract to extract text from PDFs that do not have a native text layer.

What formats can extracted tables be saved in?

Extracted tables can be converted into CSV, JSON, or formatted as Markdown tables for immediate use in documentation.

Does it support batch processing of large documents?

While it can process large files, it is recommended to use page range options for very large documents to optimize memory usage and processing speed.

Which Python libraries are used for the analysis?

The skill utilizes PyPDF2 for metadata, tabula-py for robust table extraction (requires Java), and pdfplumber for high-precision layout and font analysis.

Scanner PDF Analysis

Name: Scanner PDF Analysis
Author: takemi-ohama

bytakemi-ohama

Data Science & ML

Analyzes PDF documents to extract structured data, including tables, section headers, and metadata, while providing automated summaries.

About

This skill empowers Claude to process complex PDF documents such as technical reports, academic papers, and invoices. It performs high-precision text extraction and OCR, specifically focusing on converting embedded tables into CSV or JSON formats and identifying document hierarchies through font-size based section detection. Whether you need a concise summary of a lengthy research paper or structured data from financial statements, this skill provides a robust programmatic interface to handle PDF content directly within your workflow.

Key Features

Table detection and conversion to CSV/JSON/Markdown formats
Section and heading identification for structural document mapping
Support for OCR to process scanned image-based PDF documents
0 GitHub stars
Automated summarization of key points and metadata extraction
Multi-engine layout analysis using pdfplumber and tabula-py

Use Cases

Generating summaries and key takeaways from academic research papers
Parsing invoice data for automated expense tracking and calculation
Extracting requirement tables from technical specifications into spreadsheets

About

Key Features

Table detection and conversion to CSV/JSON/Markdown formats
Section and heading identification for structural document mapping
Support for OCR to process scanned image-based PDF documents
0 GitHub stars
Automated summarization of key points and metadata extraction
Multi-engine layout analysis using pdfplumber and tabula-py

Use Cases

Generating summaries and key takeaways from academic research papers
Parsing invoice data for automated expense tracking and calculation
Extracting requirement tables from technical specifications into spreadsheets