Extract PDF text in your browser with LiteParse for the web

2026-04-23 · Source: Simon Willison's Weblog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

On April 23, 2026, a browser-based version of LlamaIndex's LiteParse tool was released, enabling in-browser PDF text extraction. This web application, available at https://simonw.github.io/liteparse/, utilizes PDF.js and Tesseract.js to perform spatial text parsing, intelligently ordering text from complex PDF layouts, and falling back to OCR for image-based text. Unlike its original Node.js CLI counterpart, this version processes PDFs entirely client-side, ensuring no data leaves the user's machine. The development process heavily relied on AI assistants like Claude Code and Opus 4.7, demonstrating an "agentic engineering" approach where the AI generated the bulk of the code, including UI elements, testing with Playwright, and GitHub Actions for deployment, with minimal human intervention in code review.

Key takeaway

For AI Engineers or developers needing to extract structured text from PDFs while ensuring data privacy, LiteParse for the web offers a robust, client-side solution. Your teams can integrate this tool for applications requiring secure, in-browser PDF processing, potentially enhancing RAG system credibility with visual citations. Consider exploring agentic engineering patterns with AI assistants for accelerating similar web-based tool development.

Key insights

LiteParse for the web enables client-side PDF text extraction using spatial parsing and OCR, built with significant AI assistance.

Principles

• Spatial text parsing improves text order from complex PDFs.
• Client-side processing enhances data privacy and security.

Method

The web app was built using an AI assistant (Claude Code) to generate HTML, TypeScript, and deployment workflows, guided by iterative prompts and a detailed plan, with Playwright for red/green TDD.

In practice

• Use LiteParse for web for secure, in-browser PDF text extraction.
• Explore AI assistants for rapid web application prototyping.
• Implement visual citations with bounding boxes for RAG credibility.

Topics

• LiteParse
• PDF Text Extraction
• Spatial Text Parsing
• Browser Applications
• Tesseract OCR

Code references

• run-llama/liteparse
• simonw/liteparse
• simonw/claude-code-transcripts

Best for: Software Engineer, AI Engineer

Related on AIssential

• LiteParse: Local Document Parsing for AI Agents — LiteParse is an open-source, local-first parsing tool for spatial text extraction from diverse document types.
• run-llama / liteparse — LiteParse offers fast, local, and flexible document parsing with spatial text and OCR capabilities across multiple platforms.
• Ai_extract in Databricks — Databricks' `AI_extract` function, combined with regex and SQL, efficiently extracts structured data from PDF-derived HTML tables.
• opendataloader-project / opendataloader-pdf — OpenDataLoader PDF offers high-accuracy, open-source PDF parsing for AI data extraction and accessibility.
• Article: Redesigning Banking PDF Table Extraction: A Layered Approach with Java — Reliable PDF table extraction in finance requires a layered architectural approach, not just a single parsing library.
• Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality — Effective RAG parsing requires understanding both PDF structural signals and page content, augmented by an LLM-generated semantic summary.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.