A fast, helpful, and open-source document parser

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Looking for LiteParse V1? Follow this link to the old code

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, 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

• Fast Text Parsing: Spatial text parsing using PDFium
• 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
• Screenshot Generation: Generate high-quality page screenshots for LLM agents
• Multiple Output Formats: JSON and Text
• Bounding Boxes: Precise text positioning information
• Multi-language: Use from Rust, Node.js/TypeScript, Python, or the browser (WASM)
• Multi-platform: Linux, macOS (Intel/ARM), Windows

flowchart LR
      subgraph Input["Input Formats"]
          direction TB
          PDF["PDF"]
          DOCX["DOCX"]
          XLSX["XLSX"]
          PPTX["PPTX"]
          IMG["Images"]
      end

      subgraph Core["Rust Core"]
          direction TB
          CONV["Format Conversion\nLibreOffice / ImageMagick"]
          EXTRACT["Text Extraction\nPDFium C library"]
          OCR["Selective OCR\nTesseract / HTTP / Custom"]
          MERGE["OCR Merge\nNative text + OCR results"]
          PROJ["Grid Projection\nSpatial layout reconstruction"]
          CONV --> EXTRACT
          EXTRACT --> OCR --> MERGE --> PROJ
          EXTRACT --> MERGE
      end

      subgraph Output[" Output "]
          direction TB
          JSON["Structured JSON\ntext + bounding boxes"]
          TEXT["Plain Text\nlayout-preserved"]
          SCREEN["Screenshots\nPNG rendering"]
      end

      subgraph Bindings["Language Bindings"]
          direction TB
          NAPI["Node.js / TypeScript\nnapi-rs"]
          PYO3["Python\nPyO3"]
          WASM["Browser / WASM\nwasm-bindgen"]
          CLI["CLI\ncargo / npm / pip"]
          NAPI ~~~ PYO3 ~~~ WASM ~~~ CLI
      end

      PDF --> EXTRACT
      DOCX & XLSX & PPTX & IMG --> CONV
      PROJ --> JSON & TEXT & SCREEN
      JSON & TEXT & SCREEN --> Bindings

      style Input fill:#F5F5F5,color:#000000,stroke:#37D7FA,stroke-width:2px
      style Core fill:#F5F5F5,color:#000000,stroke:#3E18F9,stroke-width:2px
      style Output fill:#F5F5F5,color:#000000,stroke:#FF8705,stroke-width:2px
      style Bindings fill:#F5F5F5,color:#000000,stroke:#FF8DF2,stroke-width:2px

      style PDF fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style DOCX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style XLSX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style PPTX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style IMG fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px

      style CONV fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style EXTRACT fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style OCR fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style MERGE fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style PROJ fill:#4B72FE,color:#FFFFFF,stroke:#3E18F9,stroke-width:2px

      style JSON fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
      style TEXT fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
      style SCREEN fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px

      style NAPI fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
      style PYO3 fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
      style WASM fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
      style CLI fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px

Install via your preferred package manager. All versions (except WASM) ship with the same lit CLI.

You can use liteparse as an agent skill, downloading it with the skills CLI tool:

npx skills add run-llama/llamaparse-agent-skills --skill liteparse

Or copy-pasting the SKILL.md file to your own skills setup.

The CLI is the same across all installations (npm, pip, cargo install).

# Basic parsing
lit parse document.pdf

# Parse with specific format
lit parse document.pdf --format json -o output.json

# Parse specific pages
lit parse document.pdf --target-pages "1-5,10,15-20"

# Parse without OCR
lit parse document.pdf --no-ocr

# Parse a remote PDF
curl -sL https://example.com/report.pdf | lit parse -

Parse an entire directory of documents:

lit batch-parse ./input-directory ./output-directory

Screenshots are essential for LLM agents to extract visual information that text alone cannot capture.

# Screenshot all pages
lit screenshot document.pdf -o ./screenshots

# Screenshot specific pages
lit screenshot document.pdf --target-pages "1,3,5" -o ./screenshots

# Custom DPI
lit screenshot document.pdf --dpi 300 -o ./screenshots

lit parse [OPTIONS] <file>

Options:
  -o, --output <file>          Output file path
      --format <format>        Output format: json|text [default: text]
      --no-ocr                 Disable OCR
      --ocr-language <lang>    OCR language, Tesseract format [default: eng]
      --ocr-server-url <url>   HTTP OCR server URL (uses Tesseract if not provided)
      --tessdata-path <path>   Path to tessdata directory
      --max-pages <n>          Max pages to parse [default: 1000]
      --target-pages <pages>   Pages to parse (e.g., "1-5,10,15-20")
      --dpi <dpi>              Rendering DPI [default: 150]
      --preserve-small-text    Keep very small text
      --password <password>    Password for encrypted documents
      --num-workers <n>        Concurrent OCR workers [default: CPU cores - 1]
  -q, --quiet                  Suppress progress output
  -h, --help                   Print help

lit batch-parse [OPTIONS] <input-dir> <output-dir>

Options:
      --format <format>        Output format: json|text [default: text]
      --no-ocr                 Disable OCR
      --ocr-language <lang>    OCR language [default: eng]
      --ocr-server-url <url>   HTTP OCR server URL
      --tessdata-path <path>   Path to tessdata directory
      --max-pages <n>          Max pages per file [default: 1000]
      --dpi <dpi>              Rendering DPI [default: 150]
      --recursive              Recursively search input directory
      --extension <ext>        Only process files with this extension (e.g., ".pdf")
      --password <password>    Password for encrypted documents
      --num-workers <n>        Concurrent OCR workers
  -q, --quiet                  Suppress progress output
  -h, --help                   Print help

lit screenshot [OPTIONS] <file>

Options:
  -o, --output-dir <dir>       Output directory [default: ./screenshots]
      --target-pages <pages>   Pages to screenshot (e.g., "1,3,5" or "1-5")
      --dpi <dpi>              Rendering DPI [default: 150]
      --password <password>    Password for encrypted documents
  -q, --quiet                  Suppress progress output
  -h, --help                   Print help

Tesseract is bundled and works out of the box:

lit parse document.pdf                    # OCR enabled by default
lit parse document.pdf --ocr-language fra # Specify language
lit parse document.pdf --no-ocr           # Disable OCR

For offline or air-gapped environments, set TESSDATA_PREFIX to a directory containing pre-downloaded .traineddata files:

export TESSDATA_PREFIX=/path/to/tessdata
lit parse document.pdf --ocr-language eng

Or pass the path directly:

lit parse document.pdf --tessdata-path /path/to/tessdata

For higher accuracy or better performance, you can use an HTTP OCR server. We provide ready-to-use example wrappers for popular OCR engines:

• EasyOCR
• PaddleOCR

You can integrate any OCR service by implementing the simple LiteParse OCR API specification (see OCR_API_SPEC.md).

The API requires:

• POST /ocr endpoint
• Accepts file and language parameters
• Returns JSON: { results: [{ text, bbox: [x1,y1,x2,y2], confidence }] }

LiteParse supports automatic conversion of various document formats to PDF before parsing.

• Word: .doc, .docx, .docm, .odt, .rtf, .pages
• PowerPoint: .ppt, .pptx, .pptm, .odp, .key
• Spreadsheets: .xls, .xlsx, .xlsm, .ods, .csv, .tsv, .numbers

Install LibreOffice for automatic conversion:

# macOS
brew install --cask libreoffice

# Ubuntu/Debian
apt-get install libreoffice

# Windows
choco install libreoffice-fresh

On Windows, you may need to add LibreOffice's program directory (usually C:\Program Files\LibreOffice\program) to your PATH.

• Formats: .jpg, .jpeg, .png, .gif, .bmp, .tiff, .webp, .svg

Install ImageMagick for image-to-PDF conversion:

# macOS
brew install imagemagick

# Ubuntu/Debian
apt-get install imagemagick

# Windows
choco install imagemagick.app

The project is a Rust workspace with the core library and language-specific binding crates.

crates/
├── liteparse/          # Core library + CLI binary
├── liteparse-napi/     # Node.js bindings (napi-rs)
├── liteparse-python/   # Python bindings (PyO3)
├── liteparse-wasm/     # WASM bindings (wasm-bindgen)
├── pdfium/             # PDFium Rust wrapper
└── pdfium-sys/         # PDFium FFI bindings
packages/
├── node/               # npm package (TS wrapper + native binary)
├── python/             # PyPI package (Python wrapper + native binary)
└── wasm/               # WASM npm package

# Build the CLI
cargo build --release -p liteparse

# Build Node.js bindings
cd packages/node && npm run build

# Build Python bindings
cd packages/python && maturin develop --release

# Build WASM
cd packages/wasm && npm run build

We provide a fairly rich AGENTS.md/CLAUDE.md that we recommend using to help with development + coding agents.

Apache 2.0

Built on top of:

• PDFium - PDF rendering and text extraction
• Tesseract - OCR engine (via tesseract-rs)
• EasyOCR - HTTP OCR server (optional)
• PaddleOCR - HTTP OCR server (optional)
• napi-rs - Node.js native bindings
• PyO3 - Python native bindings
• wasm-bindgen - WebAssembly bindings