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* Expose `rec_keys_path` in RapidOcrOptions to support custom dictionaries - Added `rec_keys_path` to `RapidOcrOptions` to align with RapidOCR's capability to use custom character dictionaries. - Passed `rec_keys_path` to `RapidOcrModel` initialization, ensuring the recognition model can load the correct dictionary (e.g., for Latin characters). Signed-off-by: Yorick Terweijden <yorick@spread.ai> * style(rapidocr-options): fix alignment of `rec_keys_path` comment Adjusted the alignment of the comment for `rec_keys_path` to maintain consistent formatting. No functional changes were made. Signed-off-by: Yorick Terweijden <yorick@spread.ai> --------- Signed-off-by: Yorick Terweijden <yorick@spread.ai> |
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Docling
Docling parses documents and exports them to the desired format with ease and speed.
Features
- 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
- 📑 Advanced PDF document understanding including page layout, reading order & table structures
- 🧩 Unified, expressive DoclingDocument representation format
- 🤖 Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
- 🔍 OCR support for scanned PDFs
- 💻 Simple and convenient CLI
Explore the documentation to discover plenty examples and unlock the full power of Docling!
Coming soon
- ♾️ Equation & code extraction
- 📝 Metadata extraction, including title, authors, references & language
Installation
To use Docling, simply install docling from your package manager, e.g. pip:
pip install docling
Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
More detailed installation instructions are available in the docs.
Getting started
To convert individual documents, use convert(), for example:
from docling.document_converter import DocumentConverter
source = "https://arxiv.org/pdf/2408.09869" # document per local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"
More advanced usage options are available in the docs.
Documentation
Check out Docling's documentation, for details on installation, usage, concepts, recipes, extensions, and more.
Examples
Go hands-on with our examples, demonstrating how to address different application use cases with Docling.
Integrations
To further accelerate your AI application development, check out Docling's native integrations with popular frameworks and tools.
Get help and support
Please feel free to connect with us using the discussion section.
Technical report
For more details on Docling's inner workings, check out the Docling Technical Report.
Contributing
Please read Contributing to Docling for details.
References
If you use Docling in your projects, please consider citing the following:
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {Docling Technical Report},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
doi = {10.48550/arXiv.2408.09869},
version = {1.0.0},
year = {2024}
}
License
The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.
IBM ❤️ Open Source AI
Docling has been brought to you by IBM.