Vision, OCR, and Azure Extraction

This guide distinguishes four different features that are often conflated:

1. Built-in image metadata/description
2. Official markitdown-ocr vision plugin
3. Azure Document Intelligence
4. Azure Content Understanding

All examples target MarkItDown 0.1.6.

Decision Guide

None of the first four options is a local Tesseract workflow.

Data-Handling Rule

Before using an external service:

• Identify the exact provider, endpoint, region, account, and model/analyzer.
• Tell the user which source bytes, images, audio, video, and prompts will leave the machine.
• Confirm that the provider is approved for the source's classification and regulatory requirements.
• Estimate cost and retention implications.
• Send only the required files/pages.
• Never log API keys, bearer tokens, source bytes, or full base64 payloads.

Built-in Image Descriptions

The built-in JPEG/PNG and PPTX paths can call an OpenAI-compatible client. MarkItDown encodes image bytes as a data URI and calls:

client.chat.completions.create(model=..., messages=...)

Install a reviewed client version:

uv pip install "markitdown[pptx]==0.1.6" "openai==2.41.1"

from markitdown import MarkItDown
from openai import OpenAI

# The SDK obtains only its named provider credential through its normal
# configuration. The image and prompt are sent to that provider.
client = OpenAI()

converter = MarkItDown(
    llm_client=client,
    llm_model="gpt-4o",
    llm_prompt=(
        "Describe the scientific figure. Transcribe visible labels, identify "
        "axes and units, and report trends without inventing missing values."
    ),
)

result = converter.convert_local("figure.png")
print(result.markdown)

Use a provider/model approved by the user; model identifiers and availability are provider-specific.

Limitations

• Built-in image conversion accepts JPEG and PNG.
• Without ExifTool or an LLM client, output can be empty.
• A description is not guaranteed OCR or quantitative chart extraction.
• Generated descriptions can hallucinate labels, values, or relationships.
• Always compare critical claims with the original image.

Official markitdown-ocr Plugin

Version 0.1.6 introduced the official monorepo plugin. The published plugin version is 0.1.0.

Install exact versions:

uv pip install \
  "markitdown==0.1.6" \
  "markitdown-ocr==0.1.0" \
  "openai==2.41.1"

Review discovery before activation:

markitdown --list-plugins

Configure through Python:

from markitdown import MarkItDown
from openai import OpenAI

converter = MarkItDown(
    enable_plugins=True,
    llm_client=OpenAI(),
    llm_model="gpt-4o",
    llm_prompt=(
        "Extract all visible text exactly. Preserve table rows, columns, "
        "symbols, signs, decimal points, and units. Do not summarize."
    ),
)

result = converter.convert_local("scanned-paper.pdf")
print(result.markdown)

Supported plugin paths

• PDF embedded images, interleaved by page position
• Full-page rendering fallback for scanned PDF pages without extractable text
• PyMuPDF rendering fallback for some malformed PDFs
• DOCX embedded images
• PPTX image shapes, placeholders, and grouped images
• XLSX worksheet images

OCR blocks are inserted using markers similar to:

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

Operational behavior

• The plugin registers enhanced converters at priority -1.0, ahead of built-ins.
• Every selected image/page can become a separate provider call.
• If a provider call fails, conversion can continue without that image's OCR.
• If no llm_client is supplied, the plugin loads but silently falls back to standard conversion.
• Large scanned documents can be expensive and slow because pages are rendered at 300 DPI.

CLI discrepancy in 0.1.6

The plugin README shows --llm-client and --llm-model, but MarkItDown 0.1.6's core CLI parser does not define those options. Use the Python API above rather than copying that CLI example.

Azure Document Intelligence

Install:

uv pip install "markitdown[az-doc-intel]==0.1.6"

The converter sends the complete file to Azure's prebuilt-layout analyzer and requests Markdown output. For PDF/images it enables formula extraction, high-resolution OCR, and font-style analysis.

Authentication

If no explicit credential is supplied, MarkItDown:

1. Uses the named AZURE_API_KEY value with AzureKeyCredential when present.
2. Otherwise uses DefaultAzureCredential.

Prefer workload identity, managed identity, or another DefaultAzureCredential source over long-lived keys.

CLI

markitdown report.pdf \
  --use-docintel \
  --endpoint "https://RESOURCE.cognitiveservices.azure.com/" \
  -o report.md

The CLI requires a filename; stdin is not accepted with this mode.

Python

from azure.identity import DefaultAzureCredential
from markitdown import MarkItDown

converter = MarkItDown(
    docintel_endpoint="https://RESOURCE.cognitiveservices.azure.com/",
    docintel_credential=DefaultAzureCredential(),
)

result = converter.convert_local("report.pdf")
print(result.markdown)

Restrict routing:

from markitdown import MarkItDown
from markitdown.converters import DocumentIntelligenceFileType

converter = MarkItDown(
    docintel_endpoint="https://RESOURCE.cognitiveservices.azure.com/",
    docintel_file_types=[
        DocumentIntelligenceFileType.PDF,
        DocumentIntelligenceFileType.PNG,
    ],
)

Supported enum values include DOCX, PPTX, XLSX, HTML, PDF, JPEG, PNG, BMP, and TIFF. The default list excludes HTML.

The 0.1.6 default Document Intelligence API version is 2024-07-31-preview; override it with docintel_api_version only after checking Azure compatibility.

Azure Content Understanding

Install:

uv pip install "markitdown[az-content-understanding]==0.1.6"

Content Understanding provides:

• Document/image/audio/video analyzers
• Prebuilt analyzer auto-routing
• Optional custom analyzers
• Structured fields serialized as YAML front matter
• One endpoint across supported modalities

Every routed convert()/convert_local() call is an Azure API call and may be billable.

CLI

markitdown interview.mp4 \
  --use-cu \
  --cu-endpoint "https://RESOURCE.cognitiveservices.azure.com/" \
  --cu-file-types mp4 \
  -o interview.md

With a custom analyzer:

markitdown invoice.pdf \
  --use-cu \
  --cu-endpoint "https://RESOURCE.cognitiveservices.azure.com/" \
  --cu-analyzer "my-invoice-analyzer" \
  --cu-file-types pdf \
  -o invoice.md

Python

from azure.identity import DefaultAzureCredential
from markitdown import MarkItDown
from markitdown.converters import ContentUnderstandingFileType

converter = MarkItDown(
    cu_endpoint="https://RESOURCE.cognitiveservices.azure.com/",
    cu_credential=DefaultAzureCredential(),
    cu_file_types=[
        ContentUnderstandingFileType.PDF,
        ContentUnderstandingFileType.PNG,
    ],
)

result = converter.convert_local("report.pdf")
print(result.markdown)

Custom analyzer:

converter = MarkItDown(
    cu_endpoint="https://RESOURCE.cognitiveservices.azure.com/",
    cu_credential=DefaultAzureCredential(),
    cu_analyzer_id="my-contract-analyzer",
    cu_file_types=[ContentUnderstandingFileType.PDF],
)

When the custom analyzer's modality is incompatible with an input, the converter falls back to the matching prebuilt analyzer.

Default prebuilt routing

Choosing Between Azure Services

Validation for OCR/Cloud Output

1. Record the package/plugin version, provider, model/analyzer, endpoint region, and date.
2. Compare a sample of pages against the source.
3. Check minus signs, decimal points, Greek letters, superscripts, units, and table boundaries.
4. Flag uncertain or illegible spans instead of silently normalizing them.
5. Reconcile page counts and section headings.
6. Keep the original artifact and provider response provenance.

Sources

• MarkItDown 0.1.6 guide: https://github.com/microsoft/markitdown/blob/v0.1.6/README.md
• OCR plugin 0.1.0: https://github.com/microsoft/markitdown/tree/v0.1.6/packages/markitdown-ocr
• Document Intelligence converter: https://github.com/microsoft/markitdown/blob/v0.1.6/packages/markitdown/src/markitdown/converters/_doc_intel_converter.py
• Content Understanding converter: https://github.com/microsoft/markitdown/blob/v0.1.6/packages/markitdown/src/markitdown/converters/_cu_converter.py