# LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis

arXiv: https://arxiv.org/abs/2103.15348 · HTML: https://mlsanity.com/paper/2103.15348/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://mlsanity.com/paper/2004.12629/) (2020)
- [Image-to-Markup Generation with Coarse-to-Fine Attention](https://mlsanity.com/paper/1609.04938/) (2016)
- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [A Large Dataset of Historical Japanese Documents with Complex Layouts](https://mlsanity.com/paper/2004.08686/) (2020)
- [The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America](https://mlsanity.com/paper/2005.01583/) (2020)
- [A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis](https://mlsanity.com/paper/1905.09113/) (2019)
- [DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments](https://mlsanity.com/paper/1805.00329/) (2018)
- [OLALA: Object-Level Active Learning for Efficient Document Layout Annotation](https://mlsanity.com/paper/2010.01762/) (2020)

## Built on by (most cited first, 30 of 58)

- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [PDFVQA: A New Dataset for Real-World VQA on PDF Documents](https://mlsanity.com/paper/2304.06447/) (2023)
- [VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups](https://mlsanity.com/paper/2106.00676/) (2021)
- [PP-StructureV2: A Stronger Document Analysis System](https://mlsanity.com/paper/2210.05391/) (2022)
- [DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer](https://mlsanity.com/paper/2201.11438/) (2022)
- [Noise-Robust De-Duplication at Scale](https://mlsanity.com/paper/2210.04261/) (2022)
- [OpenChemIE: An Information Extraction Toolkit For Chemistry Literature](https://mlsanity.com/paper/2404.01462/) (2024)
- [SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation](https://mlsanity.com/paper/2305.04609/) (2023)
- [RoDLA: Benchmarking the Robustness of Document Layout Analysis Models](https://mlsanity.com/paper/2403.14442/) (2024)
- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Efficient OCR for Building a Diverse Digital History](https://mlsanity.com/paper/2304.02737/) (2023)
- [Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation](https://mlsanity.com/paper/2405.19255/) (2024)
- [GUI Action Narrator: Where and When Did That Action Take Place?](https://mlsanity.com/paper/2406.13719/) (2024)
- [SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation](https://mlsanity.com/paper/2305.00795/) (2023)
- [Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition](https://mlsanity.com/paper/2410.13305/) (2024)
- [PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy](https://mlsanity.com/paper/2505.20429/) (2025)
- [OCR Synthetic Benchmark Dataset for Indic Languages](https://mlsanity.com/paper/2205.02543/) (2022)
- [DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification](https://mlsanity.com/paper/2407.03830/) (2024)
- [CommonForms: A Large, Diverse Dataset for Form Field Detection](https://mlsanity.com/paper/2509.16506/) (2025)
- [TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets](https://mlsanity.com/paper/2201.01654/) (2022)
- [Quantifying Character Similarity with Vision Transformers](https://mlsanity.com/paper/2305.14672/) (2023)
- [PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction](https://mlsanity.com/paper/2310.03777/) (2023)
- [On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval](https://mlsanity.com/paper/2311.00693/) (2023)
- [UnSupDLA: Towards Unsupervised Document Layout Analysis](https://mlsanity.com/paper/2406.06236/) (2024)
- [DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation](https://mlsanity.com/paper/2406.17591/) (2024)
- [Latent Diffusion for Guided Document Table Generation](https://mlsanity.com/paper/2408.09800/) (2024)
- [DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral](https://mlsanity.com/paper/2505.03214/) (2025)
- [MolMole: Molecule Mining from Scientific Literature](https://mlsanity.com/paper/2505.03777/) (2025)
- [DIGMAPPER: A Modular System for Automated Geologic Map Digitization](https://mlsanity.com/paper/2506.16006/) (2025)
- [RT-DocLayout: Real-Time End-to-End Document Layout Analysis with Reading Order in the Wild](https://mlsanity.com/paper/2606.23344/) (2026)

## Abstract (arXiv)

Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation. However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy reuse of important innovations by a wide audience. Though there have been on-going efforts to improve reusability and simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper introduces layoutparser, an open-source library for streamlining the usage of DL in DIA research and applications. The core layoutparser library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks. To promote extensibility, layoutparser also incorporates a community platform for sharing both pre-trained models and full document digitization pipelines. We demonstrate that layoutparser is helpful for both lightweight and large-scale digitization pipelines in real-word use cases. The library is publicly available at https://layout-parser.github.io/.