mlsanityData to 7 Oct 2026

Docling Technical Report

arXiv 2408.09869 ↗First posted 19 Aug 2024cs.CL cs.CV cs.SEcited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

7 of 21 references.

References · 7

  1. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 526
  2. 2022TableFormer: Table Structure Understanding with Transformerscited by 93
  3. 2024LAB: Large-Scale Alignment for ChatBotscited by 39
  4. 2023Optimized Table Tokenization for Table Structure Recognitioncited by 34
  5. 2023CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Datacited by 21
  6. 2022Delivering Document Conversion as a Cloud Service with High Throughput and Responsivenesscited by 7
  7. 2024Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 14; 2026: 13.

29 papers.

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  1. 2025SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversioncited by 51
  2. 2024Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Modelscited by 12
  3. 2025Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimizationcited by 10
  4. 2025MarkushGrapher: Joint Visual and Textual Recognition of Markush Structurescited by 5
  5. 2025Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language modelscited by 3
  6. 2025HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Storescited by 3
  7. 2024Data-Prep-Kit: getting your data ready for LLM application developmentcited by 2
  8. 2025Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corporacited by 2
  9. 2025MMORE: Massive Multimodal Open RAG & Extractioncited by 2
  10. 2026Data Science and Technology Towards AGI Part I: Tiered Data Managementcited by 2
  11. 2025Making Sense of Data in the Wild: Data Analysis Automation at Scalecited by 1
  12. 2025Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processingcited by 1
17 more
  1. 2025From Answers to Guidance: A Proactive Dialogue System for Legal Documentscited by 1
  2. 2026From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answeringcited by 1
  3. 2026Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Productioncited by 1
  4. 2025SubGrapher: Visual Fingerprinting of Chemical Structurescited by 0
  5. 2025AQUA: A Large Language Model for Aquaculture & Fisheriescited by 0
  6. 2025Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answeringcited by 0
  7. 2025Exploration of Augmentation Strategies in Multi-modal Retrieval-Augmented Generation for the Biomedical Domain: A Case Study Evaluating Question Answering in Glycobiologycited by 0
  8. 2026From Evidence-Based Medicine to Knowledge Graph: Retrieval-Augmented Generation for Sports Rehabilitation and a Domain Benchmarkcited by 0
  9. 2026CoFrGeNet: Continued Fraction Architectures for Language Generationcited by 0
  10. 2026De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rulescited by 0
  11. 2026Retrieval-Augmented Reasoning for Chartered Accountancycited by 0
  12. 2026RealDocBench: A Benchmark for Field-Level QA and Layout Understanding on Real-World Regulated Documentscited by 0
  13. 2026Evidence Attribution in Visual Document Understanding without Coordinates or Region Labelscited by 0
  14. 2026The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corporacited by 0
  15. 2026Comparing Chunking and Embedding Strategies for Turkish RAG Systemscited by 0
  16. 2026From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Developmentcited by 0
  17. 2026DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answeringcited by 0