# LayoutReader: Pre-training of Text and Layout for Reading Order Detection

arXiv: https://arxiv.org/abs/2108.11591 · HTML: https://mlsanity.com/paper/2108.11591/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 26 Aug 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [Extracting Scientific Figures with Distantly Supervised Neural Networks](https://mlsanity.com/paper/1804.02445/) (2018)

## Built on by (most cited first, 30 of 80)

- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [KOSMOS-2.5: A Multimodal Literate Model](https://mlsanity.com/paper/2309.11419/) (2023)
- [PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model](https://mlsanity.com/paper/2510.14528/) (2025)
- [MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm](https://mlsanity.com/paper/2506.05218/) (2025)
- [OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition](https://mlsanity.com/paper/2403.19128/) (2024)
- [OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation](https://mlsanity.com/paper/2407.19056/) (2024)
- [XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding](https://mlsanity.com/paper/2203.06947/) (2022)
- [ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding](https://mlsanity.com/paper/2210.06155/) (2022)
- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model](https://mlsanity.com/paper/2512.02498/) (2025)
- [Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs](https://mlsanity.com/paper/2311.13194/) (2023)
- [LANS: A Layout-Aware Neural Solver for Plane Geometry Problem](https://mlsanity.com/paper/2311.16476/) (2023)
- [Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction](https://mlsanity.com/paper/2310.11016/) (2023)
- [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding](https://mlsanity.com/paper/2212.09621/) (2022)
- [Towards Visual Text Grounding of Multimodal Large Language Model](https://mlsanity.com/paper/2504.04974/) (2025)
- [LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding](https://mlsanity.com/paper/2305.18721/) (2023)
- [Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis](https://mlsanity.com/paper/2401.11874/) (2024)
- [READoc: A Unified Benchmark for Realistic Document Structured Extraction](https://mlsanity.com/paper/2409.05137/) (2024)
- [MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns](https://mlsanity.com/paper/2511.10390/) (2025)
- [XDoc: Unified Pre-training for Cross-Format Document Understanding](https://mlsanity.com/paper/2210.02849/) (2022)
- [Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding](https://mlsanity.com/paper/2409.19672/) (2024)
- [Relational Representation Learning in Visually-Rich Documents](https://mlsanity.com/paper/2205.02411/) (2022)
- [DLAFormer: An End-to-End Transformer For Document Layout Analysis](https://mlsanity.com/paper/2405.11757/) (2024)
- [Towards Few-shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework](https://mlsanity.com/paper/2204.05819/) (2022)
- [Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding](https://mlsanity.com/paper/2601.20430/) (2026)
- [SuperRAG: Beyond RAG with Layout-Aware Graph Modeling](https://mlsanity.com/paper/2503.04790/) (2025)
- [Multimodal Tree Decoder for Table of Contents Extraction in Document Images](https://mlsanity.com/paper/2212.02896/) (2022)
- [Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering](https://mlsanity.com/paper/2309.14389/) (2023)

## Abstract (arXiv)

Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images. Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection. Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model. It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments. We will release the dataset and model at urlhttps://aka.ms/layoutreader.