Docling with GoogleOCR

Docling parses documents and exports them to the desired format with ease and speed. This package differs from the main Docling package as it also includes the Google OCR option.

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
• 🤖 Easy integration with 🦙 LlamaIndex & 🦜🔗 LangChain for powerful RAG / QA applications
• 🔍 OCR support for scanned PDFs with multiple ocr engines.
• 💻 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
• 🦜🔗 Native LangChain extension

Installation

To use Docling, simply install extended-docling from your package manager, e.g. pip:

pip install extended-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.datamodel.base_models import InputFormat
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions, GoogleOcrOptions

pipeline_options = PdfPipelineOptions()
pipeline_options.ocr_options = GoogleOcrOptions()
pipeline_options.ocr_options.force_full_page_ocr = True
doc_converter = DocumentConverter(
    format_options={
        InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    }
)

source = "https://arxiv.org/pdf/2408.09869"
result = doc_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.