liteparse

K-Dense-AI/scientific-agent-skillsSearch & Data ExtractionAudit passedSnapshot cdd987fb2824

Installs: 1

Summary

Local document and PDF parsing with spatial text and bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; OCR on scans; layout-preserved JSON for RAG; batch-ingesting paper folders; or page screenshots for multimodal agents — even when the user does not name liteparse. Prefer over MarkItDown when you need bboxes, fast local parsing, or PNG page renders; prefer over the pdf skill for merge/split/forms.

SKILL.md

LiteParse — Local Document Parsing

Overview

LiteParse is a fast, open-source document parser (Rust core, Python/Node bindings) focused on local, layout-aware text extraction with bounding boxes. It does not produce Markdown and does not call cloud LLMs. Outputs are plain text (layout-preserved) or structured JSON with per-page text_items (position, font metadata, optional confidence).

Version note: Examples target liteparse 2.0.0 (PyPI, May 2026). The upstream V1 branch is legacy; this skill documents V2 / main only.

For parser selection vs MarkItDown, the pdf skill, 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

Task	Use instead

uv pip install "liteparse==2.0.0"

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

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")

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

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

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

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

lit parse document.pdf -o output.txt

import json
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

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"

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

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

from pathlib import Path

parser = LiteParse(dpi=150, quiet=True)
shots = parser.screenshot("document.pdf", page_numbers=[1, 2, 3])
out = Path("screenshots")
out.mkdir(exist_ok=True)
for s in shots:
    (out / f"page_{s.page_num}.png").write_bytes(s.image_bytes)

lit screenshot document.pdf --target-pages "1,3,5" -o ./screenshots
lit screenshot document.pdf --dpi 300 -o ./screenshots

lit batch-parse ./papers ./parsed --format json --recursive
lit batch-parse ./papers ./parsed --extension .pdf --no-ocr

python scripts/batch_parse_dir.py ./papers ./parsed --format json --recursive

parser = LiteParse(
    ocr_enabled=True,
    ocr_language="eng",       # Tesseract codes: fra, deu, etc.
    num_workers=4,            # parallel OCR (default: CPU cores - 1)
    dpi=150,                  # higher DPI → better OCR, slower
)

lit parse scan.pdf --ocr-language fra
lit parse scan.pdf --no-ocr
lit parse scan.pdf --ocr-server-url http://localhost:8080/ocr

parser = LiteParse(password="secret", quiet=True)
result = parser.parse("protected.pdf")

lit parse protected.pdf --password secret

from liteparse import search_items

page = result.get_page(1)
matches = search_items(page.text_items, "Materials and Methods", case_sensitive=False)

Category	Extensions (examples)	Requirement
PDF	`.pdf`	Native
Office	`.docx`, `.xlsx`, `.pptx`, `.doc`, `.odt`, …	LibreOffice
Images	`.png`, `.jpg`, `.tiff`, `.webp`, `.svg`, …	ImageMagick

File	Read when
`references/choosing_a_parser.md`	Unsure whether to use LiteParse, MarkItDown, pdf, or LlamaParse
`references/api_reference.md`	Python/TypeScript API, types, `search_items`
`references/cli_reference.md`	Full `lit` command flags
`references/output_formats.md`	JSON schema, bboxes, confidence scores
`references/ocr_and_formats.md`	Tesseract, HTTP OCR, LibreOffice, ImageMagick

Issue	Fix
Office file fails	Install LibreOffice; ensure `soffice` is on PATH (Windows: add LibreOffice `program` dir)
Image fails	Install ImageMagick; verify `convert` or `magick` works
OCR poor quality	Increase `--dpi`; try `--ocr-language`; or HTTP OCR server
OCR slow	`--no-ocr` if not needed; reduce pages; increase `num_workers`
Air-gapped OCR	`export TESSDATA_PREFIX=/path/to/tessdata` or `--tessdata-path`
`ParseError` on bytes	Ensure input is valid PDF bytes (Office bytes need a file path + conversion)

liteparse

Summary

SKILL.md

LiteParse — Local Document Parsing

Overview

When to Use This Skill

When Not to Use

Installation

Quick Start

Python

CLI

Core Workflows

1. Parse to layout-preserved text

2. Parse to structured JSON (bounding boxes)

3. Parse specific pages

4. Parse from bytes or stdin

5. Page screenshots for multimodal agents

6. Batch-parse a directory

7. OCR configuration

8. Encrypted PDFs

9. Search text items by phrase

Multi-Format Inputs

Performance Tips

Reference Files

Troubleshooting

Resources

Related skills