Extract text and bounding boxes from PDFs, Office files, and images.
npx skills add https://github.com/tassiovale/claude-code-kit --skill liteparse-tassiovale
Please help me install this Agent Skill. Skill: liteparse Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/liteparse Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill liteparse-tassiovale
LiteParse addresses the challenge of efficiently extracting text and spatial information from various document formats, enabling users to process and analyze documents with greater speed and accuracy.
To parse the document 'research_papers.pdf' and extract text with bounding boxes, run the following command:
lit parse research_papers.pdf --format json -o output.json
High-intent search queries and answers about installing and using this skill.
You can extract text with bounding boxes by running a local parse command on your PDF to output structured JSON. This structured format includes spatial information necessary for layout-aware RAG and citation grounding.
Yes, you can perform local OCR on scanned PDFs and images without cloud APIs by utilizing the bundled Tesseract dependency. This extracts text from image-based documents while maintaining spatial layout data in the JSON output.
Document parsing supports both PDF processing and Office files. It handles local extraction of text and spatial information from various formats, using LibreOffice as an optional dependency for converting Office files during the extraction process.
Yes, Python 3.10 or higher is required to run the document parser. Tesseract, ImageMagick, and LibreOffice are optional dependencies required specifically for OCR and Office file conversion during the parsing process.
The best way to extract text and images from research papers is using a local document parsing tool that outputs structured JSON with spatial text and bounding boxes. This converts large collections of papers into a structured format for rapid analysis.
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