microsoft / microsoft/markitdown
FORMULAS feature is unconditionally set in Azure Document Intelligence converter
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 186k
- Forks
- 13.7k
- Avg merge
- 1d 4h
- Merged PRs (30d)
- 49
Description
Problem
When using Azure Document Intelligence via markitdown,
DocumentAnalysisFeature.FORMULAS is always enabled, even when formula recognition is not required.
This behavior leads to degraded recognition accuracy, especially for documents that do not contain mathematical formulas.
The relevant code is here:
https://github.com/microsoft/markitdown/blob/main/packages/markitdown/src/markitdown/converters/_doc_intel_converter.py#L232
# _doc_intel_converter.py
features=[
DocumentAnalysisFeature.FORMULAS,
DocumentAnalysisFeature.TABLES,
]
Currently, FORMULAS is unconditionally included in the features list, making it impossible to disable.
Steps to Reproduce
- Configure Azure Document Intelligence and enable it in
markitdown - Analyze a document that does not contain mathematical formulas
- Observe the extracted text / structure quality
- Compare results with and without the
FORMULASfeature enabled
Expected Behavior
-
DocumentAnalysisFeature.FORMULAS should be optional and only enabled when explicitly requested
or -
It should be disabled by default for general-purpose document parsing
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in packages/markitdown/src/markitdown/converters/_doc_intel_converter.py around the features list at line 232. Trace how Azure Document Intelligence options are configured and determine how formula recognition should be made optional or disabled by default. Done means FORMULAS is not unconditionally enabled and general-purpose conversion preserves the expected extraction quality.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- azure, python
- Domain
- backend, tooling
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100