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DS4SD avatar

DS4SD/docling

0
View on GitHub↗
docling-project.github.io/docling↗

Docling

Docling is a multimodal content converter and document parser designed to transform PDFs, Office files, and HTML into structured Markdown or JSON for generative AI applications. It functions as an OCR document processor and a PDF layout analyzer that extracts tables, charts, and hierarchical structures while preserving the original page layout.

The system operates as a local-first inference engine, allowing for the processing of sensitive data in air-gapped environments without external network connectivity. It can also be deployed as an API or a Model Context Protocol server to provide parsing capabilities to external AI agents and language model frameworks.

Its capabilities include converting diverse file formats into a unified intermediate representation, utilizing optical character recognition for scanned documents, and transforming graphical charts into tables or text. The tool supports multi-format exports and includes speech-to-text capabilities for audio transcription.

A command line interface is provided for parsing documents from local paths or URLs into structured files.

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Features

  • AI-Ready Structured Conversions - Transforms complex PDFs and diverse document types into structured formats suitable for generative AI applications.
  • LLM-Ready Markdown Converters - Transforms PDFs and office documents into structured Markdown optimized for LLM and RAG consumption.
  • Local Inference Engines - Executes all parsing and transcription models on local hardware to allow operation in air-gapped environments.
  • Document Layout Analysis - Uses specialized models to identify structural elements like headers, tables, and lists to maintain document hierarchy.
  • PDF Document Analyzers - Analyzes complex PDF layouts to extract tables, charts, and hierarchical structures.
  • RAG Data Pipelines - Converts diverse document formats into machine-readable text to power RAG pipelines.
  • Optical Character Recognitions - Combines native text parsing with optical character recognition to process both digital and scanned documents.
  • OCR Document Processors - Ships a processing engine that extracts text from scanned documents and images via OCR.
  • Multimodal Content Converters - Transforms diverse file formats including audio and images into machine-readable text and structured data.
  • Multi-Format Document Parsing - Converts PDF, Office documents, HTML, EPUB, and emails into a unified machine-readable representation.
  • Optical Character Recognition - Extracts text from scanned PDFs and images to process content that is not natively selectable.
  • Local Data Processing - Parses sensitive files in air-gapped environments to ensure data privacy without relying on external cloud APIs.
  • Structured PDF Conversions - Analyzes page layout, table structures, and formulas within PDF files to preserve the original document hierarchy.
  • MCP Servers - Runs as an API or MCP server to provide parsing capabilities to external agents and applications.
  • Document Digitization Tools - Converts Office files, emails, and PDFs into unified formats like Markdown or JSON for organizational archival.
  • Table Structure Reconstructions - Analyzes visual cell boundaries and alignments to transform complex PDF tables into structured machine-readable data.
  • Chart-to-Table Parsers - Utilizes vision models to interpret graphical chart elements and convert them into descriptive text or tables.
  • Air-Gapped Execution - Processes sensitive data in air-gapped environments without requiring any external network connectivity.
  • Data Extraction And Generation - Fast document parsing and format conversion tool.
  • Data Ingestion and Parsing - Tool for parsing and exporting documents into structured formats.
  • Data Processing - Document preparation toolkit for generative AI workflows.
  • Data Processing Tools - Library to prepare diverse document formats for generative AI.
62,172 stars·4,360 forks·Python·MIT·49 views

Star history

Star history chart for ds4sd/doclingStar history chart for ds4sd/docling

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with Docling

These projects share indexed features with Docling. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • opendataloader-project/opendataloader-pdfopendataloader-project avatar

    opendataloader-project/opendataloader-pdf

    30,208View on GitHub↗

    This project is a PDF data extraction tool and document preprocessor designed to convert PDF files into structured formats such as Markdown, JSON, and HTML. It functions as an OCR document parser for scanned files, an accessibility automator for generating PDF/UA compliant metadata, and a loader for AI orchestration frameworks like LangChain. The software distinguishes itself through specialized handling of complex document elements, including the conversion of mathematical formulas into LaTeX and the generation of natural-language descriptions for charts and images. It utilizes recursive seg

    Javaa11yaccessibilityai
    View on GitHub↗30,208
  • quivrhq/megaparsequivrhq avatar

    quivrhq/megaparse

    7,389View on GitHub↗

    Megaparse is a document parsing tool and RAG data preprocessor designed to convert PDFs, Word documents, and presentations into clean text formats. It functions as a vision-based document extractor that recovers high-fidelity information from images and complex layouts to optimize data for large language model ingestion. The system employs multimodal AI and vision models to perform schema-preserving parsing, which maintains structural hierarchies such as tables and headers. It utilizes lossless structural transformation to turn layout-heavy binary files into text sequences while preserving th

    Python
    View on GitHub↗7,389
  • torakiki/pdfsamtorakiki avatar

    torakiki/pdfsam

    4,457View on GitHub↗

    pdfsam is a PDF manipulation software and desktop application designed for splitting, merging, rotating, and extracting pages from PDF documents. It functions as a PDF editor, converter, and security tool, providing capabilities to modify document structures and manage file formats. The project distinguishes itself through specialized processing capabilities, including an OCR document processor for extracting editable text from scanned images and PDF interleaving to alternate pages from multiple files. It also provides a security suite for encrypting documents, managing access permissions, an

    Javacombineextractjava
    View on GitHub↗4,457
  • axa-group/parsraxa-group avatar

    axa-group/Parsr

    6,177View on GitHub↗

    Parsr is an unstructured data extractor and document parsing pipeline that converts raw files and images into cleaned, machine-readable formats. It functions as a document layout analyzer and a pipeline for extracting structured data and labels using large language models. The system includes a document parsing visualizer, providing a graphical interface to upload documents and inspect the resulting structured data output. The project covers document digitization workflows, including layout analysis to detect headings, tables, and lists, and automated data entry through the cleaning and enri

    JavaScript
    View on GitHub↗6,177
Compare all 30 related projects→

Frequently asked questions

What does ds4sd/docling do?

Docling is a multimodal content converter and document parser designed to transform PDFs, Office files, and HTML into structured Markdown or JSON for generative AI applications. It functions as an OCR document processor and a PDF layout analyzer that extracts tables, charts, and hierarchical structures while preserving the original page layout.

What are the main features of ds4sd/docling?

The main features of ds4sd/docling are: AI-Ready Structured Conversions, LLM-Ready Markdown Converters, Local Inference Engines, Document Layout Analysis, PDF Document Analyzers, RAG Data Pipelines, Optical Character Recognitions, OCR Document Processors.

Which projects share features with ds4sd/docling?

Projects with overlapping indexed features include: opendataloader-project/opendataloader-pdf — This project is a PDF data extraction tool and document preprocessor designed to convert PDF files into structured… quivrhq/megaparse — Megaparse is a document parsing tool and RAG data preprocessor designed to convert PDFs, Word documents, and… torakiki/pdfsam — pdfsam is a PDF manipulation software and desktop application designed for splitting, merging, rotating, and… axa-group/parsr — Parsr is an unstructured data extractor and document parsing pipeline that converts raw files and images into cleaned,… dicklesworthstone/llm_aided_ocr — This project is a document digitization utility that combines traditional optical character recognition with language… opendatalab/mineru — MinerU is a document parsing pipeline designed to transform unstructured files into machine-readable, structured data.…