run-llama / liteparse

2026-02-09 · Source: Github Trending: All languages · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

LiteParse is an open-source, standalone PDF parsing tool designed for fast and light local execution, providing high-quality spatial text parsing with bounding boxes. It operates without proprietary LLM features or cloud dependencies, running entirely on the user's machine. The tool offers fast text parsing using PDFium, a flexible OCR system with built-in Tesseract and support for external HTTP OCR servers like EasyOCR or PaddleOCR, and the ability to generate high-quality page screenshots. It outputs structured JSON with bounding boxes or plain text, supports multiple languages (Rust, Node.js/TypeScript, Python, WASM), and is multi-platform compatible (Linux, macOS, Windows). LiteParse also handles automatic conversion of Office documents (Word, PowerPoint, Spreadsheets) via LibreOffice and various image formats via ImageMagick before parsing. For more complex documents, LlamaParse is suggested as a cloud-based alternative.

Key takeaway

For AI Engineers or Machine Learning Engineers building local document processing pipelines, LiteParse provides a robust, fast, and entirely local solution for extracting structured text and visual data from PDFs and other document types. You should consider integrating LiteParse to handle document parsing tasks where data privacy is paramount or cloud dependencies are undesirable, leveraging its multi-language bindings and flexible OCR system for efficient data preparation.

Key insights

LiteParse offers fast, local, and flexible document parsing with spatial text and OCR capabilities across multiple platforms.

Principles

• Prioritize local, dependency-free processing.
• Integrate flexible OCR options.
• Support diverse input/output formats.

Method

LiteParse converts various document types to PDF, extracts text via PDFium, selectively applies OCR, merges results, and reconstructs spatial layout for JSON/text output.

In practice

• Parse PDFs for text and bounding boxes.
• Generate page screenshots for LLM agents.
• Batch process directories of documents.

Topics

• PDF Parsing
• Optical Character Recognition
• Document Processing
• Local AI Tools
• Multi-language Bindings
• WASM

Code references

• run-llama/liteparse
• run-llama/llamaparse-agent-skills
• tesseract-ocr/tesseract
• wasm-bindgen/wasm-bindgen

Best for: NLP Engineer, AI Architect, AI Engineer, Machine Learning Engineer, Software 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.
• Extract PDF text in your browser with LiteParse for the web — LiteParse for the web enables client-side PDF text extraction using spatial parsing and OCR, built with significant AI assistance.
• LlamaParse vs  LLMs: Live OCR Battleground — Complex document parsing requires a hybrid approach combining traditional OCR with VLMs and structured harnesses to overcome LLM limitations.
• Ai_Parse_Document in Databricks | Pulling Text and Tabular Data from PDFs — Databricks' AI Parse Document converts unstructured documents into semi-structured HTML/JSON for easier data extraction.
• LiteParse - 100% Local PDF Parsing (No GPU) | Document Processing for RAG & AI Agents — Light Parse is a new local, open-source document parsing library from LlamaIndex, showing mixed results.
• Build a smart financial assistant with LlamaParse and Gemini 3.1 — Combining LlamaParse with Gemini models enables robust, accurate extraction and summarization of complex unstructured documents.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.