liteparse

# LiteParse

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[Docs](https://developers.llamaindex.ai/liteparse/)

English | [简体中文](README.zh-CN.md)

<img src="https://github.com/user-attachments/assets/07ba6a82-6bb1-4dea-b0ef-cad7df7d1622" alt="out" width="600">

> Looking for LiteParse V1? Follow this link to [the old code](https://github.com/run-llama/liteparse/tree/logan/liteparse-v1)

LiteParse is a standalone OSS PDF parsing tool focused exclusively on **fast and light** parsing. It provides high-quality spatial text parsing with bounding boxes, without proprietary LLM features or cloud dependencies. Everything runs locally on your machine.

**Hitting the limits of local parsing?**
For complex documents (dense tables, multi-column layouts, charts, handwritten text, or
scanned PDFs), you'll get significantly better results with [LlamaParse](https://developers.llamaindex.ai/python/cloud/llamaparse/?utm_source=github&utm_medium=liteparse),
our cloud-based document parser built for production document pipelines. LlamaParse handles the
hard stuff so your models see clean, structured data and markdown.

>  [Sign up for LlamaParse free](https://cloud.llamaindex.ai?utm_source=github&utm_medium=liteparse)

## Overview

- **Fast Text Parsing**: Spatial text parsing using PDFium, ~2-5ms per page
- **Flexible OCR System**:
  - **Built-in**: Tesseract (zero setup, bundled with the library)
  - **HTTP Servers**: Plug in any OCR server (EasyOCR, PaddleOCR, custom)
  - **Standard API**: Simple, well-defined OCR API specification
- **Complexity Detection**: Cheaply check whether a document needs OCR or heavier parsing — route, reject, or estimate cost before a full parse
- **Screenshot Generation**: Generate high-quality page screenshots for LLM agents
- **Multiple Output Formats**: Markdown, JSON, and Text
- **Markdown Output**: Structured Markdown with headings, tables, lists, images, and links — great for feeding LLMs and RAG pipelines
- **Bounding Boxes**: Precise text positioning information
- **Multi-language**: Use from Rust, Node.js/TypeScript, Python, or the browser (WASM)
- **Worker Pool Mode** (Python & Node.js): Parse in persistent worker processes for true parallelism (PDFium otherwise serializes concurrent parses) and hard per-parse timeouts — rogue documents