# Docling Technical Report

arXiv: https://arxiv.org/abs/2408.09869 · HTML: https://mlsanity.com/paper/2408.09869/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [LAB: Large-Scale Alignment for ChatBots](https://mlsanity.com/paper/2403.01081/) (2024)
- [Optimized Table Tokenization for Table Structure Recognition](https://mlsanity.com/paper/2305.03393/) (2023)
- [CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data](https://mlsanity.com/paper/2304.14953/) (2023)
- [Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness](https://mlsanity.com/paper/2206.00785/) (2022)
- [Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs](https://mlsanity.com/paper/2406.19102/) (2024)

## Built on by (most cited first, 30 of 34)

- [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://mlsanity.com/paper/2503.11576/) (2025)
- [Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models](https://mlsanity.com/paper/2412.13702/) (2024)
- [Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization](https://mlsanity.com/paper/2510.05038/) (2025)
- [MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures](https://mlsanity.com/paper/2503.16096/) (2025)
- [Data Science and Technology Towards AGI Part I: Tiered Data Management](https://mlsanity.com/paper/2602.09003/) (2026)
- [Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language models](https://mlsanity.com/paper/2502.14901/) (2025)
- [HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores](https://mlsanity.com/paper/2509.21336/) (2025)
- [Data-Prep-Kit: getting your data ready for LLM application development](https://mlsanity.com/paper/2409.18164/) (2024)
- [Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corpora](https://mlsanity.com/paper/2505.08905/) (2025)
- [MMORE: Massive Multimodal Open RAG & Extraction](https://mlsanity.com/paper/2509.11937/) (2025)
- [SLIDERS: Systematic Reviews via Automated Evidence Synthesis and Reconciliation](https://mlsanity.com/paper/2604.22294/) (2026)
- [Making Sense of Data in the Wild: Data Analysis Automation at Scale](https://mlsanity.com/paper/2502.15718/) (2025)
- [AQUA: A Large Language Model for Aquaculture & Fisheries](https://mlsanity.com/paper/2507.20520/) (2025)
- [Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing](https://mlsanity.com/paper/2509.04469/) (2025)
- [From Answers to Guidance: A Proactive Dialogue System for Legal Documents](https://mlsanity.com/paper/2510.19723/) (2025)
- [From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answering](https://mlsanity.com/paper/2604.04948/) (2026)
- [Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production](https://mlsanity.com/paper/2605.18818/) (2026)
- [The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora](https://mlsanity.com/paper/2608.09093/) (2026)
- [Comparing Chunking and Embedding Strategies for Turkish RAG Systems](https://mlsanity.com/paper/2608.26192/) (2026)
- [SubGrapher: Visual Fingerprinting of Chemical Structures](https://mlsanity.com/paper/2504.19695/) (2025)
- [Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering](https://mlsanity.com/paper/2508.18093/) (2025)
- [Exploration of Augmentation Strategies in Multi-modal Retrieval-Augmented Generation for the Biomedical Domain: A Case Study Evaluating Question Answering in Glycobiology](https://mlsanity.com/paper/2512.16802/) (2025)
- [From Evidence-Based Medicine to Knowledge Graph: Retrieval-Augmented Generation for Sports Rehabilitation and a Domain Benchmark](https://mlsanity.com/paper/2601.00216/) (2026)
- [CoFrGeNet: Continued Fraction Architectures for Language Generation](https://mlsanity.com/paper/2601.21766/) (2026)
- [De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rules](https://mlsanity.com/paper/2604.02276/) (2026)
- [Retrieval-Augmented Reasoning for Chartered Accountancy](https://mlsanity.com/paper/2605.00257/) (2026)
- [RealDocBench: A Benchmark for Field-Level QA and Layout Understanding on Real-World Regulated Documents](https://mlsanity.com/paper/2606.07401/) (2026)
- [Evidence Attribution in Visual Document Understanding without Coordinates or Region Labels](https://mlsanity.com/paper/2607.24651/) (2026)
- [From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development](https://mlsanity.com/paper/2609.11493/) (2026)
- [DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answering](https://mlsanity.com/paper/2609.20574/) (2026)

## Abstract (arXiv)

This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. 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. The code interface allows for easy extensibility and addition of new features and models.