LiteParse

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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

Overview

• 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

• 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)
• 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

Installation

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

| Language | Install | Library Docs | |----------|---------|--------------| | Node.js / TypeScript | npm i -g @llamaindex/liteparse | Node.js README | | Python | pip install liteparse | Python README | | Rust | cargo install liteparse (CLI) / cargo add liteparse (lib) | Rust README (crates.io) | | Browser (WASM) | npm i @llamaindex/liteparse-wasm | WASM README |

Agent Skill

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.

CLI Usage

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

Parse Files

# Basic parsing
lit parse document.pdf
Parse to Markdown — headings, tables, lists, images, links
lit parse document.pdf --format markdown -o output.md
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 -

Markdown Output

LiteParse can render documents directly to Markdown. This means reconstructing headings, tables, lists, images, and links from the spatial layout. This is ideal for feeding documents to LLMs and RAG pipelines. This mode is purely heuristics and rule-based, so complex documents may not render perfectly, but it will be fast.

# Render to Markdown
lit parse document.pdf --format markdown -o output.md
Strip images instead of emitting placeholders
lit parse document.pdf --format markdown --image-mode off
Extract embedded images to disk and reference them from the markdown
lit parse document.pdf --format markdown --image-mode embed --image-output-dir ./images
Emit link text as plain text (no text syntax)
lit parse document.pdf --format markdown --no-links

Image handling is controlled by --image-mode:

| Mode | Behavior | |------|----------| | placeholder (default) | Emits references in reading order | | off | Strips images entirely | | embed | Writes each image's PNG bytes to --image-output-dir and references them |

Markdown reconstruction quality varies with document complexity. For the

hardest documents (dense tables, multi-column layouts, scans),

LlamaParse

remains the most accurate option.

Check Complexity

Before committing to a full parse, check whether a document actually needs OCR or heavier processing. This is a cheap, text-layer-only pass — useful for routing documents to different pipelines, rejecting ones you can't handle, or estimating cost.

# Print the complexity verdict and per-page JSON
lit is-complex document.pdf
Use as a shell predicate — only parse with --no-ocr when the document is simple
lit is-complex document.pdf --quiet && lit parse document.pdf --no-ocr
List the pages that need OCR
lit is-complex document.pdf --compact | jq '[.[] | select(.needsocr) | .pagenumber]'

It always prints per-page JSON to stdout, a human-readable verdict to stderr, and exits non-zero when any page needs OCR. Each page carries a needs_ocr verdict and a list of reasons (scanned, no-text, sparse-text, embedded-images, garbled, vector-text).

Batch Parsing

Parse an entire directory of documents:

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

Generate Screenshots

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

CLI Reference

Parse Command

lit parse [OPTIONS] <file>
Options:   -o, --output <file>          Output file path       --format <format>        Output format: json|text|markdown [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]       --image-mode <mode>      Markdown image handling: off|placeholder|embed [default: placeholder]       --image-output-dir <dir> Where to write images when --image-mode embed       --no-links               Emit link anchor text as plain text (no text) in markdown       --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

Batch Parse Command

lit batch-parse [OPTIONS] <input-dir> <output-dir>
Options:       --format <format>        Output format: json|text|markdown [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

Screenshot Command

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

Is-Complex Command

lit is-complex [OPTIONS] <file>
Options:       --compact                Emit dense, whitespace-free JSON instead of pretty-printed       --max-pages <n>          Max pages to check [default: 1000]       --target-pages <pages>   Pages to check (e.g., "1-5,10,15-20")       --password <password>    Password for encrypted documents   -q, --quiet                  Suppress the stderr verdict   -h, --help                   Print help

Prints per-page JSON to stdout and a COMPLEX/SIMPLE verdict to stderr; exits non-zero when any page needs OCR, so it composes as a shell predicate.

OCR Setup

Default: Tesseract

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

Optional: HTTP OCR Servers

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 OCRAPI_SPEC.md).

The API requires:

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

Multi-Format Input Support

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

Supported Input Formats

Office Documents (via LibreOffice)

• 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.

Images (via ImageMagick)

• 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

Environment Variables

| Variable | Description | |----------|-------------| | TESSDATA_PREFIX | Path to a directory containing Tesseract .traineddata files. Used for offline/air-gapped environments. |

Development

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

Building

# 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.

License

Apache 2.0

Credits

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