PDF with docling

1.py

from docling.document_converter import DocumentConverter

source = "./page_1.pdf"

converter = DocumentConverter()
doc = converter.convert(source).document
print(doc.export_to_markdown())

1.txt

Downloading recognition model, please wait. This may take several minutes depending upon your network connection.
## Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

Nikolaos Livathinos * , Christoph Auer * , Maksym Lysak, Ahmed Nassar, Michele Dolfi, Panagiotis Vagenas, Cesar Berrospi, Matteo Omenetti, Kasper Dinkla, Yusik Kim, Shubham Gupta, Rafael Teixeira de Lima, Valery Weber, Lucas Morin, Ingmar Meijer, Viktor Kuropiatnyk, Peter W. J. Staar

IBM Research, R¨ uschlikon, Switzerland

Please send correspondence to: [email protected]

## Abstract

We introduce Docling , an easy-to-use, self-contained, MITlicensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. Docling is released as a Python package and can be used as a Python API or as a CLI tool. Docling's modular architecture and efficient document representation make it easy to implement extensions, new features, models, and customizations. Docling has been already integrated in other popular open-source frameworks (e.g., LangChain, LlamaIndex, spaCy), making it a natural fit for the processing of documents and the development of high-end applications. The open-source community has fully engaged in using, promoting, and developing for Docling, which gathered 10k stars on GitHub in less than a month and was reported as the No. 1 trending repository in GitHub worldwide in November 2024.

With Docling , we recently open-sourced a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition that we developed and presented in the recent past (Livathinos et al. 2021; Pfitzmann et al. 2022; Lysak et al. 2023). Docling is designed as a simple, self-contained Python library with permissive MIT license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models. Since its launch in July 2024, Docling has attracted considerable attention in the AI developer community and ranks top on GitHub's monthly trending repositories with more than 10,000 stars at the time of writing. On October 16, 2024, Docling reached a major milestone with version 2, introducing several new features and concepts, which we outline in this updated technical report, along with details on its architecture, conversion speed benchmarks, and comparisons to other open-source assets.

Repository -https://github.com/DS4SD/docling

## 1 Introduction

Converting documents back into a unified machineprocessable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which often discards structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs, Office documents, and scanned document images has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, SaaS offerings on hyperscalers (Auer et al. 2022) and most recently, multimodal vision-language models. Typically, they incur a cost (e.g., for licensing or LLM inference) and cannot be run easily on local hardware. Meanwhile, only a handful of different open-source tools cover PDF, MS Word, MS PowerPoint, Images, or HTML conversion, leaving a significant feature and quality gap to proprietary solutions.

* These authors contributed equally.

Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

The following list summarizes the features currently available on Docling:

- · Parses common document formats (PDF, Images, MS Office formats, HTML) and exports to Markdown, JSON, and HTML.
- · Applies advanced AI for document understanding, including detailed page layout, OCR, reading order, figure extraction, and table structure recognition.
- · Establishes a unified DoclingDocument data model for rich document representation and operations.
- · Provides fully local execution capabilities making it suitable for sensitive data and air-gapped environments.
- · Has an ecosystem of plug-and-play integrations with prominent generative AI development frameworks, including LangChain and LlamaIndex.
- · Can leverage hardware accelerators such as GPUs.

## 2 State of the Art

Document conversion is a well-established field with numerous solutions already available on the market. These solutions can be categorized along several key dimensions, including open vs. closed source, permissive vs. restrictive licensing, Web APIs vs. local code deployment, susceptibility