LiteParse — Local Document Parsing

Overview

LiteParse is an open-source document parser (Rust core, Python/Node bindings) for local, layout-aware text extraction. It produces layout text, structured JSON, or heuristic Markdown. Spatial text items may span several words; Python emit_word_boxes=True adds word boxes when needed.

Verified release: Python liteparse 2.15.0 (September 29, 2026); CLI and synthetic local PDF/image fixtures tested on Python 3.13. Node/Rust examples below are source-checked and illustrative. Images convert through bundled Rust libraries, not ImageMagick. No cloud account is needed, but missing Tesseract language data can download from GitHub and an explicitly configured HTTP OCR service receives document images.

For parser selection vs MarkItDown, PDF manipulation libraries, or LlamaParse, see references/choosing_a_parser.md.

When to Use This Skill

Use LiteParse when you need:

• Fast local parsing of PDFs or converted Office/image files without cloud dependencies
• Spatial text with bounding boxes for layout-aware RAG, citation grounding, or figure/table region logic
• OCR on scanned PDFs or images (bundled Tesseract, or a user-run HTTP OCR server)
• Page screenshots (PNG) for multimodal agents that must see charts, figures, or handwriting
• Batch ingestion of literature folders, supplementary PDFs, or protocol libraries
• Page subsets or password-protected PDFs

When Not to Use

Installation

uv pip install "liteparse==2.15.0"

This installs the Python bindings and the lit CLI. Verify:

lit --help
python -c "import liteparse; print(liteparse.__version__)"

Optional system tool (for Office inputs):

• LibreOffice — Word, Excel, PowerPoint, OpenDocument, CSV/TSV

PNG, JPEG, TIFF, WebP, SVG and other supported images convert natively.

Install commands are in references/ocr_and_formats.md.

Node.js / TypeScript (optional): npm i @llamaindex/liteparse@2.15.0 — see references/api_reference.md.

Quick Start

Python

from liteparse import LiteParse

parser = LiteParse(quiet=True)
result = parser.parse("paper.pdf")
print(result.text)

for page in result.pages:
    print(f"Page {page.page_num}: {len(page.text_items)} items")

CLI

# Layout-preserved text (default)
lit parse paper.pdf

# Structured JSON with bounding boxes
lit parse paper.pdf --format json -o paper.json

# Heuristic Markdown, including headings, tables and links
lit parse paper.pdf --format markdown -o paper.md

# Disable OCR on text-native PDFs (faster)
lit parse paper.pdf --no-ocr

Core Workflows

1. Parse to layout-preserved text

Best for quick full-document text or feeding chunkers that do not need coordinates.

parser = LiteParse(ocr_enabled=True, quiet=True)
result = parser.parse("document.pdf")
full_text = result.text

lit parse document.pdf -o output.txt

2. Parse to structured JSON (bounding boxes)

Use when building layout-aware RAG, highlighting source regions, or joining text with screenshots.

from liteparse import LiteParse

parser = LiteParse(output_format="json", quiet=True)
result = parser.parse("document.pdf")

# Programmatic access
for page in result.pages:
    for item in page.text_items:
        bbox = (item.x, item.y, item.width, item.height)
        # item.text, item.confidence, item.font_name, item.font_size

lit parse document.pdf --format json -o document.json

JSON field layout: references/output_formats.md.

3. Parse specific pages

parser = LiteParse(target_pages="1-5,10,15-20", quiet=True)
result = parser.parse("long_paper.pdf")

lit parse long_paper.pdf --target-pages "1-5,10"

4. Parse from bytes or stdin

Useful for uploads, S3 downloads, or piping remote PDFs.

with open("document.pdf", "rb") as f:
    result = parser.parse(f.read())

curl -sL https://example.com/report.pdf | lit parse -

5. Page screenshots for multimodal agents

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