marker vs Ollama-OCR
Local document extraction tools compared
marker and Ollama-OCR both convert documents into text or structured outputs using locally run models. marker focuses on document conversion with layout-aware processing and multiple input formats, while Ollama-OCR sends images and PDFs to vision-language models served through Ollama.
marker: Convert Documents into Structured Text
Marker converts PDFs and other documents into Markdown, JSON, HTML, or chunks, preserving structure such as tables, equations, and images. It suits developers building document-processing workflows who can run its local models and inference backend.
Ollama-OCR: Extract Text from Images and PDFs with Vision Models
Ollama-OCR uses vision-language models served by Ollama to extract text and structured content from images and PDFs. It offers a Python package for single or batch processing and a Streamlit interface for interactive use.
| marker | Ollama-OCR | |
|---|---|---|
| Language | Python | Jupyter Notebook |
| License | Apache-2.0 | MIT |
| Stars | 40.2k | 2.8k |
| Forks | 2.9k | 323 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- marker supports PDFs and, with additional dependencies, formats such as DOCX, XLSX, HTML, and EPUB; Ollama-OCR processes images and PDFs.
- marker combines text-layer extraction, layout detection, and OCR, using a vision-language model selectively; Ollama-OCR uses vision-language models available through Ollama.
- marker offers Markdown, JSON, HTML, and chunked output; Ollama-OCR offers formats including plain text, structured data, key-value pairs, and tables, plus custom extraction prompts.
- marker is licensed under Apache-2.0, with model weights under separate modified OpenRAIL-M terms; Ollama-OCR is licensed under MIT.
- marker provides a CLI, Python API, and small local API server; Ollama-OCR provides a Python API and a Streamlit interface.
- marker reports 40.2k stars and 2.9k forks; Ollama-OCR reports 2.8k stars and 323 forks.
Choose marker if you…
- need to convert PDFs and a range of other document formats into structured outputs.
- want layout-aware extraction that can preserve elements such as tables, equations, code, and images.
- need a CLI or Python workflow with options for custom processors and renderers.
Choose Ollama-OCR if you…
- already use Ollama and want image or PDF extraction through its vision models.
- need custom prompts to extract specified fields or request a particular text language.
- want a Streamlit interface for uploads, previews, batch processing, and downloadable results.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.