LiteParse Python

Python bindings for LiteParse — fast, lightweight PDF and document parsing with spatial text extraction.

Installation

pip install liteparse

This also installs the lit CLI command.

Quick Start

from liteparse import LiteParse

parser = LiteParse()
result = parser.parse("document.pdf")
print(result.text)
print(f"Source document pages: {result.total_pages}")

# Access structured data
for page in result.pages:
    print(f"Page {page.page_num}: {len(page.text_items)} text items")

Markdown Output

LiteParse can render documents directly to Markdown including headings, tables, lists, images, and links reconstructed from the spatial layout. Great for feeding LLMs and RAG pipelines. The rendered Markdown is returned on result.text:

parser = LiteParse(
    output_format="markdown",   # "json" | "text" | "markdown"
    image_mode="placeholder",   # "placeholder" | "off" | "embed"
    extract_links=True,         # render [text](url) link syntax (default: True)
)
result = parser.parse("document.pdf")
print(result.text)  # rendered Markdown

Reconstruction quality varies with document complexity.

Configuration

All options are passed to the constructor:

parser = LiteParse(
    ocr_enabled=True,              # Enable OCR (default: True)
    ocr_language="eng",            # Tesseract language code
    ocr_server_url=None,           # HTTP OCR server URL (optional)
    tessdata_path=None,            # Path to tessdata directory (optional)
    max_pages=1000,                # Max pages to parse
    target_pages="1-5,10",         # Specific pages (optional)
    extract_screenshots=False,      # Return parsed pages as PNG bytes
    continue_on_page_error=False,   # Skip broken pages and return page_errors
    dpi=150,                       # Rendering DPI
    output_format="json",          # "json" | "text" | "markdown"
    image_mode="placeholder",      # Markdown image handling: "placeholder" | "off" | "embed"
    extract_images=True,           # Extract image bytes + metadata (default: False)
    image_output_dir="./images",   # Write images and return name/path metadata (optional)
    extract_links=True,            # Render [text](url) links in markdown output
    keep_headers_footers=False,    # Keep running headers/footers in markdown output
    extract_vector_graphics=False, # Opt-in shapes + merged H/V lines per page
    extract_annotations=False,     # Include page annotations in structured output
    extract_form_fields=False,      # Include AcroForm widget fields and values
    extract_structure_tree=False,   # Include tagged-PDF logical structure
    preserve_very_small_text=False, # Keep tiny text
    extract_text_metadata=False,    # Opt in to MCID, font metrics, colors, char codes, and trailing_space_generated
    password=None,                 # Password for protected documents
    quiet=False,                   # Suppress progress output
    num_workers=4,                 # Concurrent OCR workers
)

When extract_images=True, image extraction is enabled. image_output_dir requires that explicit opt-in and writes the extracted bytes to disk. Each result.images entry includes its page bbox, intrinsic pixel dimensions, rotation, format, name, and path. Valid source JPEGs are preserved, exact duplicates reuse one file, and JSON CLI output contains metadata only (no base64 image data). image_mode controls Markdown presentation only and does not imply extraction. With extract_images=False, lightweight Markdown placement refs are still collected and result.images stays empty.

When extract_annotations is enabled, each parsed page has an annotations list containing the subtype, contents, author/title, PDF date strings, viewport-space rectangle and quadpoint rectangles, and URI for external link annotations. It is independent of extract_links, which controls Markdown link rendering. The field is None when extraction is disabled.

When extract_structure_tree=True, each page has a structure_tree containing all tagged-PDF roots and recursive elements with type, ID, actual/alternate text, title, typed attributes, MCIDs, children, and referenced link annotations. Untagged pages have an empty roots list; the field is None when disabled.

Every result also carries creator/producer from the PDF /Info dictionary. With extract_document_metadata=True, result.doc_meta adds a provenance object with dates, PDF version/security, signature state, incremental-save markers, trailer ID comparison, the catalog's XMP packet (capped at 64 KiB; skipped for sources over 16 MiB), and source size. It is off by default because it streams the whole source file, and it is None for inputs converted from a non-PDF format. These document fields are API-only and do not alter default CLI JSON.

Parsing from Bytes

Pass raw PDF bytes directly — useful for web uploads or downloaded files:

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

Worker Pool and Hard Timeouts

PDFium is not thread-safe, so in-process parses serialize on a process-global lock. For high-throughput services, or cases where you need to enforce a timeout, run parses in a pool of persistent worker processes instead:

from liteparse import LiteParse, ParseTimeoutError

parser = LiteParse(pool_size=4, parse_timeout=15)
parser.warm_up()  # optional: pre-initialize workers (~60ms total)

try:
    result = parser.parse("document.pdf")
except ParseTimeoutError as e:
    print(f"killed rogue document: {e.source} (deadline {e.timeout}s)")

parser.close()  # or use `with LiteParse(...) as parser:`

Screenshots

Generate PNG screenshots of document pages:

screenshots = parser.screenshot("document.pdf", page_numbers=[1, 2, 3])
for s in screenshots:
    print(f"Page {s.page_num}: {s.width}x{s.height}")
    with open(f"page_{s.page_num}.png", "wb") as f:
        f.write(s.image_bytes)

Document Complexity

Before committing to a full parse, check whether a document needs OCR or heavier processing. is_complex is a cheap, text-layer-only pass that returns one entry per page with a needs_ocr verdict and the signals behind it — useful for routing documents to different pipelines, rejecting ones you can't handle, or estimating cost.

parser = LiteParse()
pages = parser.is_complex("document.pdf")

if any(p.needs_ocr for p in pages):
    # Route to the OCR-enabled pipeline
    result = parser.parse("document.pdf")
else:
    # Cheap path — skip OCR entirely
    result = LiteParse(ocr_enabled=False).parse("document.pdf")

# Inspect why specific pages were flagged
for page in pages:
    if page.needs_ocr:
        print(f"Page {page.page_number}: {', '.join(page.reasons)}")

reasons is one of "scanned", "no-text", "sparse-text", "embedded-images", "garbled", "vector-text", or "annotation-text". Raw bytes work here too.

Supported Formats

• PDF (.pdf)
• Microsoft Office (.docx, .xlsx, .pptx, etc.) — requires LibreOffice
• OpenDocument (.odt, .ods, .odp) — requires LibreOffice
• Images (.png, .jpg, .tiff, etc.)
• And more!

CLI

The Python package includes the lit CLI:

lit parse document.pdf
lit parse document.pdf --format json -o output.json
lit parse document.pdf --format json --extract-annotations
lit parse document.pdf --format json --extract-form-fields
lit screenshot document.pdf -o ./screenshots
lit batch-parse ./input ./output
lit is-complex document.pdf