markitdown-ocr-parallel 0.2.2

Parallel LLM Vision OCR plugin for MarkItDown — extracts text from images in PDF, DOCX, PPTX, and XLSX files with 3x speedup via ThreadPoolExecutor batching.

MarkItDown OCR Plugin

LLM Vision plugin for MarkItDown that extracts text from images embedded in PDF, DOCX, PPTX, and XLSX files.

Key features:

• Parallel batch OCR via ThreadPoolExecutor — up to 3x faster than serial processing
• Configurable concurrency with max_workers (default 5)
• Uses the same llm_client / llm_model pattern that MarkItDown already supports
• No new ML libraries or binary dependencies required

Installation

pip install markitdown-ocr-parallel

The plugin uses whatever OpenAI-compatible client you already have:

pip install openai

Quick Start

Python API

from markitdown import MarkItDown
from openai import OpenAI

client = OpenAI()

md = MarkItDown(
    enable_plugins=True,
    llm_client=client,
    llm_model="gpt-4o",
    max_workers=5,  # new! parallel OCR (default: 5)
)

result = md.convert("document_with_images.pdf")
print(result.text_content)

Command Line

markitdown document.pdf --use-plugins --llm-client openai --llm-model gpt-4o

Any OpenAI-Compatible Provider

# Example: Alibaba Cloud Bailian (通义千问)
from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

md = MarkItDown(
    enable_plugins=True,
    llm_client=client,
    llm_model="qwen3.6-flash",
    max_workers=10,
)

Performance

Parallel batch processing significantly reduces conversion time for documents with multiple images:

Document	Images	Serial (1 worker)	Parallel (5 workers)	Speedup
Scanned contract (5 pages)	5	74.4s	24.1s	3.1x
PDF with 3 embedded images	3	4.6s	3.3s	1.4x
PDF with 2 embedded images	2	2.2s	1.2s	1.9x
Scanned report (3 pages)	3	6.6s	2.6s	2.6x

Tune max_workers based on your API rate limits and document complexity.

How It Works

When MarkItDown(enable_plugins=True, llm_client=..., llm_model=...) is called:

1. MarkItDown discovers the plugin via the markitdown.plugin entry point group
2. Four OCR-enhanced converters are registered at priority -1.0 — before built-in converters (priority 0.0)

When a file is converted, the plugin uses a collect → batch → assemble pipeline:

1. Collection Phase: All images are extracted from the document upfront (fast, no API calls)
2. Batch OCR Phase: All images are sent to the LLM in parallel via ThreadPoolExecutor
3. Assembly Phase: OCR results are inserted inline, preserving document structure

This replaces the original serial approach where each image was OCR'd one-by-one.

Supported File Formats

PDF

• Embedded images extracted by position, interleaved with text in reading order
• Scanned PDFs detected automatically — full pages rendered at 300 DPI and OCR'd
• Malformed PDFs fall back to PyMuPDF page rendering
• Parallel: all page images OCR'd simultaneously

DOCX

• Images extracted via document part relationships
• Placeholder injection pattern prevents markdown converter from escaping OCR markers
• Parallel: all images OCR'd in one batch before HTML→Markdown conversion

PPTX

• Two-round parallel: Round 1 → LLM caption for all images; Round 2 → OCR backup for failed images
• Recursive shape walking handles groups, placeholders, and picture shapes
• Parallel: images from all slides processed together

XLSX

• Images extracted per sheet with cell position tracking
• Parallel: images from all sheets collected and OCR'd in one batch

Output Format

Every extracted OCR block is wrapped as:

*[Image OCR]
<extracted text>
[End OCR]*

Configuration

Parameter	Default	Description
llm_client	—	OpenAI-compatible client (required)
llm_model	—	Model name, e.g. gpt-4o, qwen3.6-flash (required)
llm_prompt	"Extract all text..."	Custom OCR extraction prompt
max_workers	5	Max parallel OCR threads

Development

Running Tests

git clone https://github.com/echojfree/markitdown-ocr-parallel.git
cd markitdown-ocr
pip install -e ".[llm]"
pytest tests/ -v

Building

pip install build
python -m build

Changelog

0.2.0 — Parallel Batch OCR

• Parallel OCR: extract_text_batch() method using ThreadPoolExecutor for 2-3x speedup
• max_workers parameter (default 5) on LLMVisionOCRService
• PDF: single-pass image collection + batch OCR + Y-position interleaving
• DOCX: batch OCR before HTML→Markdown pipeline
• PPTX: two-round parallel (LLM caption → OCR fallback) with pre-scan phase
• XLSX: cross-sheet image collection + batch OCR
• Fix: scanned PDF fallback detection (regex-strip page headers)
• All 36 tests pass

0.1.0 — Initial Release

• LLM Vision OCR for PDF, DOCX, PPTX, XLSX
• Full-page OCR fallback for scanned PDFs
• Priority-based converter replacement

License

MIT — see LICENSE.