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

Uses the same llm_client / llm_model pattern that MarkItDown already supports for image descriptions — no new ML libraries or binary dependencies required.

• Enhanced PDF Converter: Extracts text from images within PDFs, with full-page OCR fallback for scanned documents
• Enhanced DOCX Converter: OCR for images in Word documents
• Enhanced PPTX Converter: OCR for images in PowerPoint presentations
• Enhanced XLSX Converter: OCR for images in Excel spreadsheets
• Context Preservation: Maintains document structure and flow when inserting extracted text

pip install markitdown-ocr

The plugin uses whatever OpenAI-compatible client you already have. Install one if you don't have it yet:

pip install openai

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

Pass llm_client and llm_model to MarkItDown() exactly as you would for image descriptions:

from markitdown import MarkItDown
from openai import OpenAI

md = MarkItDown(
    enable_plugins=True,
    llm_client=OpenAI(),
    llm_model="gpt-4o",
)

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

If no llm_client is provided the plugin still loads, but OCR is silently skipped — falling back to the standard built-in converter.

Override the default extraction prompt for specialized documents:

md = MarkItDown(
    enable_plugins=True,
    llm_client=OpenAI(),
    llm_model="gpt-4o",
    llm_prompt="Extract all text from this image, preserving table structure.",
)

Works with any client that follows the OpenAI API:

from openai import AzureOpenAI

md = MarkItDown(
    enable_plugins=True,
    llm_client=AzureOpenAI(
        api_key="...",
        azure_endpoint="https://your-resource.openai.azure.com/",
        api_version="2024-02-01",
    ),
    llm_model="gpt-4o",
)

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

1. MarkItDown discovers the plugin via the markitdown.plugin entry point group
2. It calls register_converters(), forwarding all kwargs including llm_client and llm_model
3. The plugin creates an LLMVisionOCRService from those kwargs
4. Four OCR-enhanced converters are registered at priority -1.0 — before the built-in converters at priority 0.0

When a file is converted:

1. The OCR converter accepts the file
2. It extracts embedded images from the document
3. Each image is sent to the LLM with an extraction prompt
4. The returned text is inserted inline, preserving document structure
5. If the LLM call fails, conversion continues without that image's text

• Embedded images are extracted by position (via page.images / page XObjects) and OCR'd inline, interleaved with the surrounding text in vertical reading order.
• Scanned PDFs (pages with no extractable text) are detected automatically: each page is rendered at 300 DPI and sent to the LLM as a full-page image.
• Malformed PDFs that pdfplumber/pdfminer cannot open (e.g. truncated EOF) are retried with PyMuPDF page rendering, so content is still recovered.

• Images are extracted via document part relationships (doc.part.rels).
• OCR is run before the DOCX→HTML→Markdown pipeline executes: placeholder tokens are injected into the HTML so that the markdown converter does not escape the OCR markers, and the final placeholders are replaced with the formatted *[Image OCR]...[End OCR]* blocks after conversion.
• Document flow (headings, paragraphs, tables) is fully preserved around the OCR blocks.

• Picture shapes, placeholder shapes with images, and images inside groups are all supported.
• Shapes are processed in top-to-left reading order per slide.
• If an llm_client is configured, the LLM is asked for a description first; OCR is used as the fallback when no description is returned.

• Images embedded in worksheets (sheet._images) are extracted per sheet.
• Cell position is calculated from the image anchor coordinates (column/row → Excel letter notation).
• Images are listed under a ### Images in this sheet: section after the sheet's data table — they are not interleaved into the table rows.

Every extracted OCR block is wrapped as:

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

The most likely cause is a missing llm_client or llm_model. Verify:

from openai import OpenAI
from markitdown import MarkItDown

md = MarkItDown(
    enable_plugins=True,
    llm_client=OpenAI(),   # required
    llm_model="gpt-4o",    # required
)

Confirm the plugin is installed and discovered:

markitdown --list-plugins   # should show: ocr

The plugin propagates LLM API errors as warnings and continues conversion. Check your API key, quota, and that the chosen model supports vision inputs.

cd packages/markitdown-ocr
pytest tests/ -v

git clone https://github.com/microsoft/markitdown.git
cd markitdown/packages/markitdown-ocr
pip install -e .

Contributions are welcome! See the MarkItDown repository for guidelines.

MIT — see LICENSE.

• LLM Vision OCR for PDF, DOCX, PPTX, XLSX
• Full-page OCR fallback for scanned PDFs
• Context-aware inline text insertion
• Priority-based converter replacement (no code changes required)