READoc: A Unified Benchmark for Realistic Document Structured Extraction
Authors: Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun
Accepted at: acl25

READOC: A Unified Benchmark for Realistic Document Structured Extraction

Zichao Li1,2,*, Aizier Abulaiti1,2,*, Yaojie Lu1, Xuanang Chen1,†,
Jia Zheng1,†, Hongyu Lin1, Xianpei Han1, Shanshan Jiang3, Bin Dong3, Le Sun1
1Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences
2University of Chinese Academy of Sciences
3Ricoh Software Research Center Beijing Co., Ltd
{lizichao2022,aizier2022,luyaojie,chexuanang,zhengjia}@iscas.ac.cn
{hongyu,xianpei,sunle}@iscas.ac.cn {shanshan.jiang,bin.dong}@cn.ricoh.com

Abstract

Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequate, significantly hindering the field’s advancement. This problem is largely attributed to existing benchmark paradigms, which exhibit fragmented and localized characteristics. To offer a thorough evaluation of DSE systems, we introduce a novel benchmark named READOC, which defines DSE as a realistic task of converting unstructured PDFs into semantically rich Markdown. The READOC dataset is derived from 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo. In addition, we develop a DSE Evaluation Suite comprising Standardization, Segmentation and Scoring modules, to conduct a unified evaluation of state-of-the-art DSE approaches. By evaluating a range of pipeline tools, expert visual models, and general Vision-Language Models, we identify the gap between current work and the unified, realistic DSE objective for the first time. We aspire that READOC will catalyze future research in DSE, fostering more comprehensive and practical solutions.

1 Introduction

The wealth of knowledge preserved in documents is immeasurable. Document Structured Extraction (DSE), which involves converting raw documents into machine-readable structured text (Tkaczyk et al., 2015; Zhong et al., 2019; Shen et al., 2022; Lo et al., 2023), is increasingly crucial in real-world scenarios. It facilitates building extensive knowledge bases (Wang et al., 2020), constructing high-quality corpora (Jain et al., 2020), and plays a pivotal role in Retrieval-Augmented Generation (RAG) (Gao et al., 2023) applications for Large Language Models (LLMs) (Achiam et al., 2023).

Recent progress in Document AI (Appalaraju et al., 2021; Huang et al., 2022; Ye et al., 2023; Hu et al., 2024) has led to the creation of numerous DSE systems (Breezedeus, 2022; Paruchuri and Lampa, 2023; Blecher et al., 2023). However, the absence of unified evaluation in real-world scenarios has left uncertainty about their performance levels and hindered their further development. This issue is largely due to the limitations of prevailing benchmark paradigms, which exhibit fragmented and unrealistic characteristics. Firstly, as depicted in Figure 1, existing benchmarks typically fragment DSE into distinct subtasks, including document layout analysis (Zhong et al., 2019), optical character recognition (Karatzas et al., 2015), table-of-contents extraction (Hu et al., 2022), reading order detection (Wang et al., 2021), table recognition (Smock et al., 2022) and formula conversion (Deng et al., 2017). Due to their narrow focus, diverse data sources, and inconsistent formats, existing benchmarks lack a unified framework to com-

![A comparison between fragmented and localized DSE task views and the READOC benchmark paradigm.](page_1012_384_484_384.png)

Figure 1: A comparison between fragmented and localized DSE task views and the READOC benchmark paradigm.
<table>
  <tr>
    <th rowspan="2">Benchmark</th>
    <th colspan="6">Coverage</th>
    <th colspan="2">Task Paradigm</th>
  </tr>
  <tr>
    <th>Layout Analysis</th>
    <th>Character Recognition</th>
    <th>Table Recognition</th>
    <th>Formula Conversion</th>
    <th>ToC. Extraction</th>
    <th>Order Detection</th>
    <th>INPUT</th>
    <th>OUTPUT</th>
  </tr>
  <tr>
    <td>PubLayNet</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>Page image</td>
    <td>Layout blocks</td>
  </tr>
  <tr>
    <td>DocBank</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>Page image & text</td>
    <td>Layout blocks</td>
  </tr>
  <tr>
    <td>Robust Reading</td>
    <td>✗</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>Text image</td>
    <td>Plain text</td>
  </tr>
  <tr>
    <td>PubTabNet</td>
    <td>✗</td>
    <td>✗</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>Table image</td>
    <td>HTML table</td>
  </tr>
  <tr>
    <td>TabLeX</td>
    <td>✗</td>
    <td>✗</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>Table image</td>
    <td>\( \LaTeX \) table</td>
  </tr>
  <tr>
    <td>Im2Latex-100K</td>
    <td>✗</td>
    <td>✗</td>
    <td>✓</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>Formula image</td>
    <td>\( \LaTeX \) formula</td>
  </tr>
  <tr>
    <td>ReadingBank</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✓</td>
    <td>✓</td>
    <td>Unsorted tokens</td>
    <td>Sorted tokens</td>
  </tr>
  <tr>
    <td>HRDoc</td>
    <td>✓</td>
    <td>✗</td>
    <td>✗</td>
    <td>✗</td>
    <td>✓</td>
    <td>✓</td>
    <td>Doc images & text</td>
    <td>Logical tree</td>
  </tr>
  <tr>
    <th>READoc</th>
    <th>✓</th>
    <th>✓</th>
    <th>✓</th>
    <th>✓</th>
    <th>✓</th>
    <th>✓</th>
    <th>Realistic PDF doc</th>
    <th>Markdown text</th>
  </tr>
</table>

Table 1: A comparison between READOC and existing DSE benchmarks, including PubLayNet (Zhong et al., 2019), DocBank (Li et al., 2020), Robust Reading (Karatzas et al., 2015), PubTabNet (Zhong et al., 2020), TabLeX (Desai et al., 2021), Im2Latex-100K (Deng et al., 2017), ReadingBank (Wang et al., 2021), and HRDoc (Ma et al., 2023).

prehensively evaluate DSE systems. Additionally, current research often unrealistically targets localized regions, such as layout blocks or tables within a single page. This approach overlooks the complexity of real-world documents, which typically span multiple pages with hierarchical headings and require long-range dependencies to construct a global structure. Benchmarks focusing on isolated pages or blocks fail to provide realistic evaluations.

To address these issues, we introduce READOC, a unified benchmark designed to quantify the gap between existing work and the goal of REAlistic Document Structured Extraction. READOC formulates DSE as an end-to-end task, converting multi-page PDFs into structured Markdown. We automatically construct 3,576 PDF-Markdown pairs from arXiv, GitHub, and Zenodo, covering diverse types, years, and topics to reflect real-world complexity. Additionally, we develop a DSE evaluation S$^3$uite with three modules: Standardization, Segmentation, and Scoring, enabling unified evaluation of diverse DSE systems, including pipeline tools (Paruchuri and Lampa, 2023), expert models (Blecher et al., 2023), and Vision-Language Models (Achiam et al., 2023).

Our contributions are three-fold: 1) READOC is the first benchmark to frame DSE as a PDF-to-Markdown paradigm, which is realistic, end-to-end, and incorporates diverse data. 2) An evaluation S$^3$uite is proposed to support the unified assessment of various DSE systems and to quantify multiple capabilities required for DSE. 3) We present the gap between current research and realistic DSE, emphasizing the importance of exploring new modeling paradigms. The code and data are publicly available at https://github.com/icip-cas/READoc.

2 Related Work

2.1 Task Views and Benchmarks

DSE is a crucial task, yet existing benchmarks focus on discrete subtasks: document layout analysis (Zhong et al., 2019; Li et al., 2020) identifies layout blocks; optical character recognition (Karatzas et al., 2015) extracts text from images; table structure recognition (Zhong et al., 2020) transforms tables into structured formats; mathematical formula conversion (Deng et al., 2017) converts formulas into semantic formats; table-of-contents (ToC) extraction (Ma et al., 2023) constructs hierarchical heading trees; reading order detection (Wang et al., 2021) sorts page elements by reading order. We summarize relevant benchmarks in Table 1. However, their heterogeneity complicates unified DSE evaluation.

Recent research conceptualizes DSE as a single-page image-to-markup task (Blecher et al., 2023; Lee et al., 2023), and targeted benchmarks such as OmniDocBench (Ouyang et al., 2024) have emerged. Although OmniDocBench demonstrates commendable richness in evaluation and diversity of data, its single-page approach falls short in handling multi-page or lengthy documents often encountered in real-world scenarios.

2.2 Methods for Document Structured Extraction

Due to the intricacies of textual, graphical, and layout information within documents (Xu et al., 2020),
a universally accepted method for DSE has yet to emerge. A common simplistic strategy involves leveraging external parsing engines (PyMuPDF, 2024) to extract text and metadata from digital-born PDFs. With the rise of deep learning techniques, Numerous systems (Li et al., 2022; Paruchuri and Lampa, 2023; Contributors, 2024) have integrated a series of submodels into a pipeline, with each submodel dedicated to a specific subtask of DSE. Recent advancements have shifted towards end-to-end DSE methodologies, with some works leveraging Transformer (Vaswani et al., 2017) expert models to convert document page images directly into structured formats such as HTML (Lee et al., 2023) or Markdown (Blecher et al., 2023).

Recently, large Vision-Language Models have garnered widespread attention (Achiam et al., 2023; Liu et al., 2024c), with document understanding (Feng et al., 2023; Hu et al., 2024) being a key focus of their capabilities. Efforts to enhance VLMs, document understanding capability have focused on methods like tailored training tasks (Ye et al., 2023) and resolution adaptation (Li et al., 2024), leading to notable improvements. VLMs obtain impressive results on various DSE subtasks, such as OCR (Liu et al., 2024d), table recognition (Zhao et al., 2024), and formula conversion (Xia et al., 2024). Additionally, some research has explored converting page images into structured text using VLMs (Lv et al., 2023; Wei et al., 2024a; Liu et al., 2024a). However, the lack of a unified benchmark leaves uncertainty about the gap between VLMs’ current capabilities and realistic DSE needs.

3 Task Definition

We establish a realistic task paradigm for end-to-end DSE, using raw PDF documents as input due to their prevalence and unstructured nature, which poses challenges with dispersed content and multimodal information. On the other hand, we employ Markdown as the output format, leveraging its lightweight markup to represent structural elements like headings and lists. We adopt a variant of Markdown (Blecher et al., 2023) that supports LaTeX syntax for tables and formulas. Markdown, as the target format, can be chunked, indexed as flat text, or directly ingested by LLMs.

In summary, READOC uniformly defines DSE as a task that takes a complete PDF file as input and generates structured text in Markdown format, which is well-defined, practical, and challenging for DSE systems. Examples of task inputs and outputs are provided in Appendix A.

4 Benchmark Construction

READOC is a unified DSE benchmark derived from real-world documents. We select heterogeneous documents from arXiv preprints¹, GitHub READMEs² and Zenodo’s Open Research Repository³, which are then automatically processed to construct PDF-Markdown pairs. READOC consists of 3,576 documents: 1,009 in the READOC-arXiv subset, 1,224 in the READOC-GitHub subset, and 1,343 in the READOC-Zenodo subset. Each subset offers unique challenges: READOC-arXiv features complex academic structures such as formulas and tables, with diverse multi-column layout templates. However, its heading styles are simple, often following easily recognizable patterns like "1.1 Introduction." In contrast, READOC-GitHub includes only basic elements like paragraphs and headings, and presents a uniform single-column layout style. However, building its ToC structure is challenging due to varied and often unmarked heading styles. READOC-Zenodo contains longer documents (many exceeding 30 pages) and diverse types, such as posters, reports, theses, and books, challenging layout analysis. Additionally, its 27 languages further increase text processing and semantic understanding complexity. Each subset exhibits significant diversity in types, topics, eras, and so on, establishing READOC as a robust benchmark. We describe more construction details in Appendix B.

4.1 Document Collection and Processing

READOC-arXiv. For the arXiv preprints, the collection process involves using type keywords such as “Conference” and “Journal” to select papers and filtering for English language. Preprints without LaTeX files or with unclear main LaTeX files are excluded. The selected documents are first converted from LaTeX to HTML using LaTeXML⁴, followed by a conversion from HTML to Markdown using a modified version of the Nougat (Blecher et al., 2023) process. Only documents that complete this process without any errors and maintain correct table syntax after conversion are included.

¹https://arxiv.org/
²https://github.com/
³https://zenodo.org/
⁴https://github.com/brucemiller/LaTeXML
Figure 2: Visualization of data distribution in READOC. (a) Document disciplines of READOC-arXiv. (b) Document topics of READOC-GitHub. (c) Language distribution of READOC-Zenodo.

<table>
  <tr>
    <th>Statistics</th>
    <th>arXiv</th>
    <th>GitHub</th>
    <th>Zenodo</th>
  </tr>
  <tr>
    <td>Documents</td>
    <td>1,009</td>
    <td>1,224</td>
    <td>1,343</td>
  </tr>
  <tr>
    <td>Avg. Pages</td>
    <td>11.67</td>
    <td>6.54</td>
    <td>14.93</td>
  </tr>
  <tr>
    <td>Avg. Depth</td>
    <td>3.10</td>
    <td>3.11</td>
    <td>2.66</td>
  </tr>
  <tr>
    <td>Avg. Length</td>
    <td>10,209.50</td>
    <td>1,978.10</td>
    <td>8,255.85</td>
  </tr>
  <tr>
    <td>Year Span</td>
    <td>1996 - 2024</td>
    <td>2008 - 2024</td>
    <td>2014 - 2024</td>
  </tr>
  <tr>
    <td>Types / Disciplines</td>
    <td>6 / 8</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Topics</td>
    <td>-</td>
    <td>2,805</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Language count</td>
    <td>1</td>
    <td>1</td>
    <td>27</td>
  </tr>
</table>

Table 2: The data statistics of READOC.

READDoc-GitHub. The GitHub README files are originally in Markdown format, and we collect and filter them based on specific criteria: they must have obtained than 500 stars, be written in English, exclude HTML syntax, etc. To maintain the simplicity of this subset, we exclude files that contain tables and formulas. After initial preprocessing, the Markdown files are converted to PDFs using Pandoc\footnote{https://github.com/jgm/pandoc} as the conversion engine and Eisvogel\footnote{https://github.com/Wandmalfarbe/pandoc-latex-template} as the template. Only documents that successfully complete this entire workflow without execution errors or warnings are retained.

READDoc-Zenodo. For the Zenodo dataset, we collect 910 DOCX and 433 HTML files from Zenodo. DOCX files are converted to Markdown using Microsoft’s Markitdown (microsoft, 2024) tool, then to PDF via python-docx\footnote{https://pypi.org/project/python-docx/}. HTML files are converted to Markdown using Pandoc\footnote{https://github.com/jgm/pandoc}, then to PDF via Chromium. We exclude conversion failures and overly long/short files, retaining only structurally rich documents.

4.2 Dataset Statistics

We present the basic statistics of READOC in Table 2, showcase the diversity of READOC in Figure 2, and provide additional statistics in Appendix B. Overall, our benchmark is divided into three subsets. READOC-arXiv consists of 1,009 documents, with an average of 11.67 pages, 10,209.50 tokens, and 3.10 heading levels. These documents cover a timeline from 1996 to 2024, ensuring ample diversity across 6 types and 8 disciplines. READOC-GitHub comprises 1,224 documents, with an average of 6.54 pages, 1,978.11 tokens, and 3.11 heading levels. These documents are sourced from projects spanning the years 2008 to 2024, encompassing a rich tapestry of 2,805 topics. READOC-Zenodo contains 1,343 documents, with an average of 14.93 pages, 8,255.85 tokens, and 2.66 heading levels. These documents span from 2014 to 2024 and include 27 languages, e.g., English and French.

5 Evaluation S$^3$uite

Considering the potential confusion that various models may have with our Markdown syntax and the multifaceted nature of the capabilities required for DSE tasks, we propose an Evaluation S$^3$uite, consisting of three sequential modules: Standardization, Segmentation, and Scoring, as shown in Figure 3. The S$^3$uite ensures that READOC can automatically perform a unified evaluation of the end-to-end DSE task, yielding reliable and effective assessment results. More implementation details are in Appendix C.

5.1 Standardization

The first module of the S$^3$uite standardizes the output Markdown text to align with the ground truth Markdown format. This alignment is essential for mitigating the impact on evaluation accuracy caused by variations in the format and syntax
<table>
  <tr>
    <th>Standardization</th>
    <th>Segmentation</th>
    <th>Scoring</th>
  </tr>
  <tr>
    <td>
      <b>Constraining Coefficient</b><br>
      ===<br>
      Coefficient<br>
      ...<br>
      \begin{equation}<br>
      D_{e}=\text{extrm}(V)^{\delta}\langle\delta\rangle<br>
      \end{equation><br>
      (see equation)<br>
      the diffusion index \( \langle\delta\rangle \) is 1/3.<br>
      index $|\delta|_{S}$ is 1/3.<br>
      ![figure_1.png](figure_1.jpg)<br>
      name[acc]<br>
      [x:--|--:]<br>
      [X|88.7]<br>
      [Y|67.8]
    </td>
    <td>
      <b># Constraining Coefficient</b><br>
      #D_e=\text{extrm}(GeV)^{-\langle\delta\rangle}\\
      the diffusion index \( \langle\delta\rangle \) is 1/3.<br>
      \begin{table}
        \begin{tabular}{c c}
          X & 88.7 \\
          Y & 67.8
        \end{tabular}
      \end{table}
    </td>
    <td>
      <b>Semantic Unit Evaluation</b><br>
      Heading Detection - Concat EDS - Tree EDS<br>
      Table Recognition - Concat EDS - Tree EDS<br>
      Reading Order Detection - Block-level KTDS - Token-level KTDS<br>
      <b>Formula Conversion</b><br>
      Formula Conversion - Embedded EDS - Isolated EDS<br>
      <b>Text Extraction</b><br>
      Text Extraction - Concat EDS - Vocab FT<br>
    </td>
  </tr>
  <tr>
    <td colspan="2">
      <b>Headings</b><br>
      <b>Formulas</b><br>
      <b>Tables</b><br>
      <b>Plain Text</b>
    </td>
    <td></td>
  </tr>
</table>

Figure 3: The three modules of the READOC Evaluation S$^3$uite: Standardization, Segmentation and Scoring.

of texts generated by different DSE systems. The standardization includes aligning the boundaries of formulas, such as $$, \begin{equation}, and \text{V}; unifying different Markdown heading styles; aligning Markdown tables with LATEX table formats for evaluation consistency; removing images and eliminating the link syntax, etc. This module ensures that the focus remains on the core DSE capabilities, rather than being clouded by formatting disunity.

5.2 Segmentation

The second module of the S$^3$uite divides the standardized Markdown text into distinct semantic units. To facilitate READOC in highlighting various specialized DSE capabilities of the models within a single document, we divide both the output and the ground truth Markdown text into four units: Headings of different levels, Formulas in both embedded and isolated forms, Tables, and residual Plain Text encompassing basic text and simple formatting such as bold, italic, and lists.

5.3 Scoring

The scoring module comprises two submodules:

The Semantic Unit Evaluation submodule implicitly assesses the models’ layout analysis ability of identifying semantic units and explicitly measures four specialized capabilities: 1) Text Extraction refers to extracting plain text through PDF bytecode parsing or visual methods (e.g., OCR tools, VLMs). We measure edit distance similarity (EDS) after concatenating plain text and the F1 score of the plain text vocabulary. 2) Heading Detection involves detecting headings and constructing a hierarchical ToC tree. We measure EDS after concatenating all headings and construct ToC trees to calculate tree edit distance similarity (TEDS). 3) Formula Conversion involves transforming mathematical formulas into LATEX format. We measure EDS by concatenating all embedded formulas, as well as all isolated formulas. 4) Table Recognition involves identifying tables’ structures. We evaluate EDS after concatenating all tables. Then, we convert tables into structural trees, use a maximum bipartite matching algorithm to find the optimal mapping between tables and calculate TEDS for the matched tables.

The Reading Order Detection submodule determines whether the model extracts document elements in the correct order. We first segment each document into blocks based on semantic unit boundaries and line breaks, create two ordered lists from these blocks, and calculate Kendall’s Tau Distance Similarity (KTDS) between the lists. Additionally, we divide each document into sequential tokens, construct lists based on the positions of co-occurring tokens at their first appearance, and compute KTDS between the token-level lists.

6 Experiments

6.1 Compared Methods

Baselines. We employ three simple and widely applicable methods as baselines. The first is PyMuPDF4LLM (PyMuPDF, 2024), a PDF bytecode parsing engine that converts digital-born PDFs into Markdown using embedded metadata. The second is Tesseract (Smith, 2007), an OCR tool for text extraction and basic page segmentation. The third is MarkItDown (microsoft, 2024), a versatile tool designed for converting various file types into Markdown format.

Pipeline Tools. We evaluate three tools that support PDF-to-Markdown functionality, which integrate complex engineering with PDF parsing engines and advanced deep learning submodels. The pipeline tools we evaluate include the following. Marker (Paruchuri and Lampa, 2023), MinerU
<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th colspan="6">Semantic Unit Evaluation</th>
    <th rowspan="2">Reading Order</th>
    <th rowspan="2">Average</th>
  </tr>
  <tr>
    <th>Text Concat</th>
    <th>Vocab</th>
    <th>Heading Concat</th>
    <th>Tree</th>
    <th>Formula Embed</th>
    <th>Isolate</th>
    <th>Table Concat</th>
    <th>Tree</th>
    <th>Block Token</th>
  </tr>
  <tr>
    <th colspan="10">Baselines</th>
  </tr>
  <tr>
    <td>PyMuPDF4LLM</td>
    <td>66.66</td>
    <td>74.27</td>
    <td><b>27.86</b></td>
    <td>20.77</td>
    <td>0.07</td>
    <td><b>0.02</b></td>
    <td>23.27</td>
    <td><b>15.83</b></td>
    <td>87.70</td>
    <td>89.09</td>
    <td><b>40.55</b></td>
  </tr>
  <tr>
    <td>Tesseract OCR</td>
    <td><b>78.85</b></td>
    <td>76.51</td>
    <td>1.26</td>
    <td>0.30</td>
    <td><b>0.12</b></td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>96.70</td>
    <td><b>97.59</b></td>
    <td>35.13</td>
  </tr>
  <tr>
    <td>MarkItDown</td>
    <td>73.88</td>
    <td><b>79.64</b></td>
    <td>2.90</td>
    <td>0.93</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.34</td>
    <td>0.08</td>
    <td><b>97.13</b></td>
    <td>97.08</td>
    <td>35.21</td>
  </tr>
  <tr>
    <th colspan="10">Pipeline Tools</th>
  </tr>
  <tr>
    <td>MinerU</td>
    <td><b>88.32</b></td>
    <td><b>91.22</b></td>
    <td>67.06</td>
    <td><b>41.97</b></td>
    <td><b>62.77</b></td>
    <td><b>70.76</b></td>
    <td>59.34</td>
    <td>52.85</td>
    <td><b>98.52</b></td>
    <td><b>97.90</b></td>
    <td><b>73.07</b></td>
  </tr>
  <tr>
    <td>Pix2Text</td>
    <td>85.85</td>
    <td>83.72</td>
    <td>63.23</td>
    <td>34.53</td>
    <td>43.18</td>
    <td>37.45</td>
    <td>54.08</td>
    <td>47.35</td>
    <td>97.68</td>
    <td>96.78</td>
    <td>64.39</td>
  </tr>
  <tr>
    <td>Marker</td>
    <td>79.11</td>
    <td>82.71</td>
    <td>63.60</td>
    <td>39.39</td>
    <td>3.47</td>
    <td>48.74</td>
    <td><b>64.61</b></td>
    <td><b>72.36</b></td>
    <td>98.04</td>
    <td>97.74</td>
    <td>64.98</td>
  </tr>
  <tr>
    <td>Docling</td>
    <td>79.73</td>
    <td>85.39</td>
    <td><b>68.74</b></td>
    <td>38.33</td>
    <td>0.23</td>
    <td>0.0</td>
    <td>54.09</td>
    <td>66.56</td>
    <td>98.05</td>
    <td>97.18</td>
    <td>58.83</td>
  </tr>
  <tr>
    <th colspan="10">Expert Visual Models</th>
  </tr>
  <tr>
    <td>Nougat-small</td>
    <td>87.35</td>
    <td>92.00</td>
    <td>86.40</td>
    <td>87.88</td>
    <td><b>76.52</b></td>
    <td>79.39</td>
    <td><b>55.63</b></td>
    <td><b>52.35</b></td>
    <td>97.97</td>
    <td>98.36</td>
    <td>81.38</td>
  </tr>
  <tr>
    <td>Nougat-base</td>
    <td><b>88.03</b></td>
    <td><b>92.29</b></td>
    <td><b>86.60</b></td>
    <td><b>88.50</b></td>
    <td>76.19</td>
    <td>79.47</td>
    <td>54.40</td>
    <td>52.30</td>
    <td><b>97.98</b></td>
    <td><b>98.41</b></td>
    <td><b>81.42</b></td>
  </tr>
  <tr>
    <td>GOT-OCR 2.0</td>
    <td>84.47</td>
    <td>86.24</td>
    <td>66.69</td>
    <td>57.68</td>
    <td>53.48</td>
    <td>56.23</td>
    <td>50.40</td>
    <td>34.50</td>
    <td>97.73</td>
    <td>97.50</td>
    <td>68.49</td>
  </tr>
  <tr>
    <th colspan="10">Vision-Language Models</th>
  </tr>
  <tr>
    <td>DeepSeek-VL-7B-Chat</td>
    <td>31.89</td>
    <td>39.96</td>
    <td>23.66</td>
    <td>12.53</td>
    <td>17.01</td>
    <td>16.94</td>
    <td>22.96</td>
    <td>16.47</td>
    <td>88.76</td>
    <td>66.75</td>
    <td>33.69</td>
  </tr>
  <tr>
    <td>MiniCPM-Llama3-V2.5</td>
    <td>58.91</td>
    <td>70.87</td>
    <td>26.33</td>
    <td>7.68</td>
    <td>16.70</td>
    <td>17.90</td>
    <td>27.89</td>
    <td>24.91</td>
    <td>95.26</td>
    <td>93.02</td>
    <td>43.95</td>
  </tr>
  <tr>
    <td>LLaVa-1.6-Vicuna-13B</td>
    <td>27.51</td>
    <td>37.09</td>
    <td>8.92</td>
    <td>6.27</td>
    <td>17.80</td>
    <td>11.68</td>
    <td>23.78</td>
    <td>16.23</td>
    <td>76.63</td>
    <td>51.68</td>
    <td>27.76</td>
  </tr>
  <tr>
    <td>InternVL-Chat-V1.5</td>
    <td>53.06</td>
    <td>68.44</td>
    <td>25.03</td>
    <td>13.57</td>
    <td>33.13</td>
    <td>24.37</td>
    <td>40.44</td>
    <td>34.35</td>
    <td>94.61</td>
    <td>91.31</td>
    <td>47.83</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td><b>79.44</b></td>
    <td><b>84.37</b></td>
    <td><b>31.77</b></td>
    <td><b>18.65</b></td>
    <td><b>42.23</b></td>
    <td><b>41.67</b></td>
    <td><b>47.81</b></td>
    <td><b>39.85</b></td>
    <td><b>97.69</b></td>
    <td><b>96.35</b></td>
    <td><b>57.98</b></td>
  </tr>
</table>

Table 3: Evaluation of various Document Structured Extraction systems on READOC-arXiv.

<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th colspan="6">Semantic Unit Evaluation</th>
    <th rowspan="2">Reading Order</th>
    <th rowspan="2">Average</th>
  </tr>
  <tr>
    <th>Text Concat</th>
    <th>Vocab</th>
    <th>Heading Concat</th>
    <th>Tree</th>
    <th>Table Concat</th>
    <th>Tree</th>
    <th>Block Token</th>
  </tr>
  <tr>
    <td>MinerU</td>
    <td>57.28</td>
    <td>59.95</td>
    <td>30.73</td>
    <td>22.83</td>
    <td>38.75</td>
    <td>26.82</td>
    <td>65.46</td>
    <td>66.87</td>
    <td>46.08</td>
  </tr>
  <tr>
    <td>Marker</td>
    <td>59.34</td>
    <td>61.68</td>
    <td>30.28</td>
    <td>18.29</td>
    <td>40.68</td>
    <td>29.10</td>
    <td>57.77</td>
    <td>66.40</td>
    <td>46.44</td>
  </tr>
  <tr>
    <td>Nougat-base</td>
    <td>57.54</td>
    <td>66.87</td>
    <td><b>35.99</b></td>
    <td><b>26.98</b></td>
    <td>13.99</td>
    <td>11.55</td>
    <td>93.56</td>
    <td>93.01</td>
    <td>49.94</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td><b>64.16</b></td>
    <td><b>71.76</b></td>
    <td>25.07</td>
    <td>15.4</td>
    <td><b>45.75</b></td>
    <td><b>31.88</b></td>
    <td><b>95.23</b></td>
    <td><b>95.2</b></td>
    <td><b>55.56</b></td>
  </tr>
</table>

Table 5: Evaluation of three representative Document Structured Extraction systems on READOC-Zenodo.

(Contributors, 2024), Pix2Text (Breezedeus, 2022) and Docling (Auer et al., 2024).

Expert Visual Models. We evaluate Nougat-small and Nougat-base (Blecher et al., 2023), specialized transformer models trained on arXiv academic documents under the single-page image-to-Markdown paradigm, with parameter sizes of 250M and 350M, respectively. Additionally, we evaluate the GOT-OCR 2.0 model (Wei et al., 2024b), a unified, end-to-end OCR system with 580M parameters.

Vision-Language Models. We evaluate VLMs using the same single-page image-to-Markdown paradigm as expert models. For efficiency, we select open-source models with fewer than 30 billion parameters and lightweight proprietary models. Only VLMs with basic instruction-following and preliminary Markdown understanding are retained. The retained VLMs include: the open-source models DeepSeek-VL-7B-Chat (Lu et al., 2024), MiniCPM-Llama3-V2.5 (Hu et al., 2023), LLaVa-1.6-Vicuna-13B (Liu et al., 2024b), InternVL-Chat-V1.5 (Chen et al., 2024), and the proprietary model GPT-4o-mini (Achiam et al., 2023). More implementation details are included in Appendix D.

6.2 Experimental Results

Results for READOC-arXiv, READOC-GitHub and READOC-Zenodo are presented in Table 3, 4 and 5, respectively. We draw insights from both DSE system categories and specialized capabilities.

From the point of DSE system categories, we observe that: 1) Pipeline tools are often plagued by complex engineering challenges. Docling falls short in recognizing embedded and isolated formulas, while Marker struggles with detecting embedded formulas, issues that are overlooked within the complex pipeline designs. In contrast, MinerU performs significantly better than other pipeline
<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th colspan="4">Semantic Unit Evaluation</th>
    <th colspan="2">Reading Order</th>
    <th rowspan="2">Avg.</th>
  </tr>
  <tr>
    <th>Text<br>Concat</th>
    <th>Vocab</th>
    <th>Heading<br>Concat</th>
    <th>Tree</th>
    <th>Block</th>
    <th>Token</th>
  </tr>
  <tr>
    <th colspan="8">Baselines</th>
  </tr>
  <tr>
    <td>PyMuPDF4LLM</td>
    <td>85.21</td>
    <td>77.27</td>
    <td>12.13</td>
    <td>11.05</td>
    <td>98.61</td>
    <td>98.43</td>
    <td><b>63.78</b></td>
  </tr>
  <tr>
    <td>Tesseract OCR</td>
    <td>82.06</td>
    <td>82.32</td>
    <td>6.65</td>
    <td>4.25</td>
    <td>98.48</td>
    <td>99.01</td>
    <td>62.13</td>
  </tr>
  <tr>
    <td>MarkItDown</td>
    <td>85.23</td>
    <td>85.69</td>
    <td>2.07</td>
    <td>0.64</td>
    <td><b>99.4</b></td>
    <td><b>99.42</b></td>
    <td>62.07</td>
  </tr>
  <tr>
    <th colspan="8">Pipeline Tools</th>
  </tr>
  <tr>
    <td>MinerU</td>
    <td>84.46</td>
    <td>84.78</td>
    <td>67.24</td>
    <td><b>47.15</b></td>
    <td><b>99.51</b></td>
    <td><b>99.18</b></td>
    <td>80.39</td>
  </tr>
  <tr>
    <td>Pix2Text</td>
    <td>78.99</td>
    <td>78.51</td>
    <td>60.53</td>
    <td>39.42</td>
    <td>97.94</td>
    <td>97.38</td>
    <td>75.46</td>
  </tr>
  <tr>
    <td>Marker</td>
    <td><b>89.50</b></td>
    <td><b>88.11</b></td>
    <td><b>72.81</b></td>
    <td>37.51</td>
    <td>99.03</td>
    <td>99.13</td>
    <td><b>81.02</b></td>
  </tr>
  <tr>
    <td>Dooling</td>
    <td>44.53</td>
    <td>50.73</td>
    <td>40.17</td>
    <td>22.8</td>
    <td>70.77</td>
    <td>70.19</td>
    <td>49.87</td>
  </tr>
  <tr>
    <th colspan="8">Expert Visual Models</th>
  </tr>
  <tr>
    <td>Nougat-small</td>
    <td>76.04</td>
    <td>77.11</td>
    <td><b>62.81</b></td>
    <td>38.73</td>
    <td>98.33</td>
    <td>96.12</td>
    <td>74.86</td>
  </tr>
  <tr>
    <td>Nougat-base</td>
    <td>75.26</td>
    <td>76.79</td>
    <td>62.39</td>
    <td>37.01</td>
    <td>97.65</td>
    <td>95.62</td>
    <td>74.12</td>
  </tr>
  <tr>
    <td>GOT-OCR 2.0</td>
    <td><b>78.18</b></td>
    <td><b>84.53</b></td>
    <td>62.08</td>
    <td><b>50.06</b></td>
    <td><b>98.40</b></td>
    <td><b>98.60</b></td>
    <td><b>78.64</b></td>
  </tr>
  <tr>
    <th colspan="8">Vision-Language Models</th>
  </tr>
  <tr>
    <td>DeepSeek-VL-7B-Chat</td>
    <td>34.85</td>
    <td>41.97</td>
    <td>38.69</td>
    <td>20.15</td>
    <td>96.47</td>
    <td>84.55</td>
    <td>52.78</td>
  </tr>
  <tr>
    <td>MiniCPM-Llama3-V2.5</td>
    <td>71.61</td>
    <td>78.88</td>
    <td>39.66</td>
    <td>22.99</td>
    <td>97.91</td>
    <td>97.97</td>
    <td>68.17</td>
  </tr>
  <tr>
    <td>LLaVa-1.6-Vicuna-13B</td>
    <td>37.03</td>
    <td>52.88</td>
    <td>27.49</td>
    <td>16.13</td>
    <td>95.65</td>
    <td>90.45</td>
    <td>53.27</td>
  </tr>
  <tr>
    <td>InternVL-Chat-V1.5</td>
    <td>72.59</td>
    <td>78.27</td>
    <td>56.85</td>
    <td>32.70</td>
    <td>98.39</td>
    <td>97.69</td>
    <td>72.75</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td><b>85.06</b></td>
    <td><b>89.41</b></td>
    <td><b>62.65</b></td>
    <td><b>42.04</b></td>
    <td><b>98.81</b></td>
    <td><b>99.05</b></td>
    <td><b>79.50</b></td>
  </tr>
</table>

Table 4: Evaluation of various DSE systems on READOC-GitHub.

![Line graphs showing method performance vs document depth and document length](page_1042_120_377_377.png)

Figure 4: Relationship between DSE systems’ performance and the depth or length of documents in READOC-GitHub.

<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th rowspan="2">Modeling Paradigm</th>
    <th colspan="4">Semantic Unit Evaluation</th>
    <th colspan="2">Reading Order</th>
    <th rowspan="2">Average</th>
  </tr>
  <tr>
    <th>Text<br>Concat</th>
    <th>Vocab</th>
    <th>Heading<br>Concat</th>
    <th>Formula<br>Embed Isolate Concat Tree</th>
    <th>Table<br>Concat</th>
    <th>Order<br>Block Token</th>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td>Single Page</td>
    <td><b>77.95</b></td>
    <td><b>83.10</b></td>
    <td>40.63</td>
    <td>23.82</td>
    <td>39.03</td>
    <td>54.05</td>
    <td>44.70</td>
    <td>97.79</td>
    <td>96.61</td>
    <td>59.82</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td>Multiple Pages</td>
    <td>70.28</td>
    <td>78.66</td>
    <td><b>62.71</b></td>
    <td><b>51.87</b></td>
    <td>33.22</td>
    <td>33.62</td>
    <td>43.50</td>
    <td>34.09</td>
    <td><b>98.87</b></td>
    <td><b>96.31</b></td>
    <td><b>60.31</b></td>
  </tr>
</table>

Table 6: Comparison of page-level modeling paradigms, for documents within 5 pages of READOC-arXiv.

models in both embedded and isolated formula recognition. Besides, Pix2Text encounters program crashes and fails to process certain PDFs (3 files on READOC-arXiv and 4 on READOC-GitHub), posing a significant usability issue. 2) Expert models struggle with generalization and scalability issues. Nougat performs well on READOC-arXiv but declines markedly on READOC-GitHub (i.e., from 81.42 to 74.12) and READOC-Zenodo (e.g., from 81.42 to 49.94), which features simpler layouts and fewer semantic units, indicating poor transfer learning ability. Moreover, scaling up from Nougat-small to Nougat-base does not boost performance (+0.04 on READOC-arXiv, -0.74 on READOC-GitHub). Additionally, while GOT-OCR 2.0 scores lower than Nougat-small and Nougat-base on READOC-arXiv, it outperforms them on the READOC-GitHub subset. 3) VLMs generally underperform in complex academic documents. The best-performing open-source model, InternVL-Chat-V1.5, scores 47.83 on READOC-arXiv, while the proprietary model GPT-4o-mini scores 57.98, both of which are lower than the pipeline tools. On READOC-Zenodo, GPT-4o-mini achieves an average score of 55.56, suggesting its potential in handling multilingual and multi-format documents.

From the perspective of specialized capabilities, we observe that: 1) Building hierarchical ToC trees from a global perspective remains a significant challenge, as existing systems predominantly focus on single-page images. Pipeline tools lack modules to assess heading depth, leading to substantial drops in Tree EDS compared to Concat EDS. Expert models can exhibit strong ToC construction for specific documents, which is more a superficial imitation rather than a semantic understanding of the logical structure. On READOC-arXiv, Nougat-base scores 88.50 in TEDS, while on READOC-GitHub, it drops to 37.01. 2) Understanding localized structured data such as tables and formulas is relatively difficult. VLMs perform poorly on these tasks. Even the expert model Nougat-base, trained on arXiv documents,
<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th colspan="3">Semantic Unit Evaluation (Avg.)</th>
  </tr>
  <tr>
    <th>Single-col.</th>
    <th>Multi-col.</th>
    <th>Drop ↓</th>
  </tr>
  <tr>
    <td>MinerU</td>
    <td>52.75</td>
    <td>48.34</td>
    <td>4.41</td>
  </tr>
  <tr>
    <td>Pix2Text</td>
    <td><b>56.93</b></td>
    <td><b>55.34</b></td>
    <td><b>1.59</b></td>
  </tr>
  <tr>
    <td>Marker</td>
    <td>56.70</td>
    <td>53.39</td>
    <td>3.31</td>
  </tr>
  <tr>
    <td>Nougat-small</td>
    <td><b>80.51</b></td>
    <td>74.09</td>
    <td>6.42</td>
  </tr>
  <tr>
    <td>Nougat-base</td>
    <td>80.48</td>
    <td><b>74.16</b></td>
    <td><b>6.32</b></td>
  </tr>
  <tr>
    <td>InternVL-Chat-V1.5</td>
    <td>39.96</td>
    <td>32.83</td>
    <td>7.13</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td><b>49.23</b></td>
    <td><b>47.08</b></td>
    <td><b>2.15</b></td>
  </tr>
</table>

Table 7: Relationship between DSE systems’ semantic unit evaluation and the layout of documents in READOC-arXiv.

<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th rowspan="2">Modeling Paradigm</th>
    <th colspan="2">Heading</th>
  </tr>
  <tr>
    <th>Concat</th>
    <th>Tree</th>
  </tr>
  <tr>
    <td>Pix2Text</td>
    <td>Single Page</td>
    <td>63.38</td>
    <td>38.89</td>
  </tr>
  <tr>
    <td>Nougat-small</td>
    <td>Single Page</td>
    <td>64.87</td>
    <td>39.53</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td>Single Page<br>Multiple Pages</td>
    <td>69.78<br><b>83.71</b></td>
    <td>45.12<br><b>68.11</b></td>
  </tr>
</table>

Table 8: Comparison of page-level modeling paradigms, for documents within 5 pages of READOC-GitHub.

has shown only modest performance, with average metrics of 65.56 on these two tasks. 3) Reading Order Detection is a relatively easy capability to acquire. The baseline tool Tesseract, which uses heuristics for page segmentation, scores 96.70 and 98.48 in block-level KTDS on READOC-arXiv and READOC-GitHub, respectively.

6.3 Fine-grained Analysis

Impact of Document Length and Depth. Figure 4 displays the results of three representative DSE systems on READOC-GitHub. Pipeline tools and expert models exhibit similar performance trends, remaining stable with variations in document length but declining sharply as document depth increases. In contrast, VLMs demonstrate stability with changes in document depth, while their performance decreases as document length increases. This indicates that different DSE systems exhibit distinct shortcomings in realistic scenarios, which have not been previously revealed.

Impact of Document Layout. Using pdflumber (Singer-Vine and The pdflumber contributors, 2024) and heuristic rules, we classify documents in READOC-arXiv into single- and multi-column categories, representing different layout complexities. Table 7 illustrates the average semantic unit evaluation scores across the two document types. All systems show performance degradation on complex multi-column documents, highlighting that our semantic unit evaluation implicitly measures the layout analysis capability. Among the systems, GPT-4o-mini exhibits the best layout analysis capability, while InternVL-Chat-V1.5 shows the most significant performance decline, reflecting substantial differences in performance levels among VLMs.

Exploration of the Multi-Page Paradigm. Previous researches focus on processing single pages and perform poorly in constructing global ToC trees. To explore how DSE systems might utilize global information, we investigate a paradigm where multiple pages are processed simultaneously. Specifically, we employ GPT-4o-mini to receive all page images of the document at once and convert them into Markdown text. We conduct experiments on documents with up to 5 pages, as shown in Tables 6 and 8. While this method significantly enhances global ToC construction compared to the single-page paradigm, processing multiple pages simultaneously reduces local fine-grained capabilities, such as table and formula conversion, indicating that DSE modeling for multi-page documents still needs further development.

Considerations of Efficiency. DSE focuses on practical efficiency, which may involve real-time RAG calls or large-scale corpus construction. We sample 50 documents from READOC-arXiv and measure the throughput of DSE systems, as shown in Table 9. Despite significant advancements in GPU memory and computational power, VLMs remains considerably lower efficiency compared to pipeline tools and expert Transformer models, indicating the need for future improvements in not only performance but also efficiency.

<table>
  <tr>
    <th>Methods</th>
    <th>Time Cost (s) per Document</th>
    <th>NVIDIA GPU Devices</th>
  </tr>
  <tr>
    <td>Marker</td>
    <td>23.86</td>
    <td></td>
  </tr>
  <tr>
    <td>MinerU</td>
    <td>30.96</td>
    <td>1× Titan RTX (24GB)</td>
  </tr>
  <tr>
    <td>Nougat-small</td>
    <td>51.34</td>
    <td></td>
  </tr>
  <tr>
    <td>Nougat-base</td>
    <td>101.07</td>
    <td></td>
  </tr>
  <tr>
    <td>Pix2Text</td>
    <td>188.10</td>
    <td></td>
  </tr>
  <tr>
    <td>MiniCPM-Llama3-V2.5</td>
    <td>392.37</td>
    <td>1× A100 (80GB)</td>
  </tr>
  <tr>
    <td>InternVL-Chat-V1.5</td>
    <td>1,182.02</td>
    <td>2× A100 (80GB)</td>
  </tr>
</table>

Table 9: Comparison of Efficiency of DSE systems.

6.4 Case Study

We compare results from four representative systems in Figure 5. Key observations include: 1)
lume.angle(x1, y1, x2, y2) Returns the angle between the two points.

lume.vector(angle, magnitude) Given an 'angle' and 'magnitude', returns a vector.

GPT-4o-mini : Misidentifying Heading Levels
lume.angle(x1, y1, x2, y2) Returns the angle between the two points.
lume.vector(angle, magnitude) Given an angle and magnitude, returns a vector.

Pix2Text : Program Execution Error
IndexError: list index out of range

READoc Ground Truth
lume.angle(x1, y1, x2, y2) Returns the angle between the two points.
lume.vector(angle, magnitude) Given an 'angle' and 'magnitude', returns a vector.

InternVL-Chat-V1.5 : Hallucinations & Inaccurate Text Extraction
lume.angle(x1, y1, x2, y2) Returns the angle between the two points.
lume.vector(angle, magnitude) Given an angle in degrees and magnitude, returns a vector.

Nougat-small : Hallucinations & Repetition
Abstract
In this paper we present a new method for computing the number of nodes in a graph. ... We also show that the number of nodes in a graph is bounded by a constant. We also show that the number of nodes in a graph is bounded by a constant. We also show ...

Figure 5: A case study from READoc-GitHub. More cases are in Appendix D.

GPT-4o-mini misclassifies heading levels, revealing the limitations of single-page paradigms that fail to globally perceive the document’s logical structure. 2) InternVL-Chat-V1.5 exhibits hallucinations and inaccurate text extraction, illustrating the differences in DSE capabilities between open-source and proprietary VLMs. 3) Nougat-small fabricates content completely unrelated to the original document, reflecting the poor generalization ability of expert models. 4) Pix2Text triggers an execution error, demonstrating the complexity in developing pipeline tools.

7 Conclusion

This paper proposes READOC, a novel benchmark that frames document structured extraction as a realistic, end-to-end task, i.e., transforming unstructured PDFs into semantically rich Markdown text. Based on an evaluation S3uite, We conduct a unified evaluation of state-of-the-art approaches, including pipeline tools, expert models and general VLMs. Our experimental results reveal critical gaps in current researches when applied to realistic scenarios and underscore the importance of exploring new research paradigms.

Limitations

Our work has two main limitations: 1) First, there exists some noise in the PDF-Markdown pairs generated through the automated framework. Although we have implemented various filtering and post-processing methods to minimize this impact, it remains difficult to eliminate completely. In fact, this is a common challenge in current document processing benchmark fields, and we will continue to explore more efficient and accurate processing paradigms in the future. 2) Second, although we introduce a Standardization module in our DSE evaluation S3uite to unify the outputs from different models to the ground truth format, there are still some scenarios we cannot fully account for. We plan to introduce more comprehensive format unification modules in future work.

Ethics Statement

All the data, tools and model weights we use come from publicly available sources. We only use them for evaluation purposes in document structured extraction. When using these resources for this study, we strictly adhere to their licensing agreements.

Acknowledgment

We sincerely thank the reviewers for their insightful comments and valuable suggestions. This work was supported by Beijing Municipal Science and Technology Project (Nos. Z231100010323002), Beijing Natural Science Foundation (L243006), the Natural Science Foundation of China (No. 62306303, 62476265) and the Basic Research Program of ISCAS (Grant No. ISCAS-JCZD-202401).

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A Task Example

This section provides an example of input and output for the Document Structured Extraction (DSE) task as defined by READOC. As illustrated in Figure 6, DSE systems are required to process a multi-page, real-world PDF document as input and produce a structured Markdown text as output.

B Details of Dataset

B.1 Construction Details

Type Keywords of READOC-arXiv. As we aim to improve data diversity in READOC across types, disciplines, topics, and so on, we use custom keywords to capture the document category types in READOC-arXiv, as illustrated in Table 10. We treat the comments of each arXiv preprint as retrieval targets, determining whether they contain the keywords to map them to specific document types. The document type distribution of READOC-arXiv is illustrated in Figure 10.

Modification of Nougat process. As described in the main body of the paper, we modify Nougat’s source code (Blecher et al., 2023) to convert arXiv documents in HTML format into Markdown text. Specifically, our modifications include: 1) improved support for converting the \tableofcontents command; 2) recognition and conversion of nested lists; 3) support for identifying and converting sub-tables and subfigures; and 4) removal of line breaks within headings in the Markdown format.

<table>
  <tr>
    <th>Document Type</th>
    <th>Keywords</th>
  </tr>
  <tr>
    <td>Workshop</td>
    <td><i>Workshop</i></td>
  </tr>
  <tr>
    <td>Conference</td>
    <td><i>Conference</i></td>
  </tr>
  <tr>
    <td>Journal</td>
    <td><i>Journal</i></td>
  </tr>
  <tr>
    <td>Dissertation</td>
    <td><i>Thesis, Dissertation</i></td>
  </tr>
  <tr>
    <td>Guide</td>
    <td><i>Handbook, Manual, Guide, Tutorial, Technical Note</i></td>
  </tr>
  <tr>
    <td>Others</td>
    <td>-</td>
  </tr>
</table>

Table 10: Document type keywords of READOC-arXiv.

B.2 Additional Data Statistics

In addition to the diversity in disciplines, topics and languages, we provide supplementary statistical information here. This includes the distribution of the READOC dataset by year, as shown in Figure 7, and the distribution by page count, depth, and length, as illustrated in Figure 8 and Figure 9.

C Details of Evaluation S$^3$uite

C.1 Standardization Details

We standardized the Markdown text output from various tools and models according to a set of specific rules:

• Alignment of different formula boundaries. For isolated formulas, we standardize the starting boundary by converting $$, \begin{equation}, \begin{gather}, \begin{multline}, \begin{equation}, to \(, with a similar format for the ending boundary. For embedded formulas, we convert the starting boundary $ to \(, with a similar format for the ending boundary.

• Heading formatting. We standardize document headings by converting them to text lines beginning with consecutive “#” symbols, where the number of “#” indicates the heading level.

• Removal of certain elements. External URLs are removed from the link format and image references are also removed.

• Table formatting. We standardize tables by converting Markdown-compliant tables to LaTeX format. The reason for using LaTeX as
the standardized table format is that, compared to native Markdown, it can represent more complex features such as multi-row and multi-column layouts.

C.2 Segmentation Details
The segmentation module primarily based on a series of regular expressions written in Python, as illustrated in Figure 11.

C.3 Scoring Details
This section mainly discusses the metric calculations involved in the scoring module. Specifically, we focus on the following three similarity measures:

1) Edit Distance Similarity (EDS): The edit distance \( ED(A, B) \) is defined as the minimum number of single-character edits (insertions, deletions, or substitutions) required to change string \( A \) into string \( B \). The edit distance similarity is calculated using the formula:

\[
EDS(A, B) = 1 - \frac{ED(A, B)}{\max(|A|, |B|)}
\]

where \( |A| \) and \( |B| \) are the lengths of strings \( A \) and \( B \), respectively.

2) Tree Edit Distance Similarity (TEDS): The tree edit distance \( TED(T_1, T_2) \) is the minimum number of operations needed to transform one tree \( T_1 \) into another tree \( T_2 \). The operations typically include insertion, deletion, and relabeling of nodes. The tree edit distance similarity is computed as follows:

\[
TEDS(T_1, T_2) = 1 - \frac{TED(T_1, T_2)}{\max(|T_1|, |T_2|)}
\]

where \( |T_1| \) and \( |T_2| \) represent the sizes of trees \( T_1 \) and \( T_2 \), respectively.

3) Kendall’s Tau Distance Similarity (KTDS): The Kendall’s Tau distance measures the ordinal association between two rankings. The number of discordant pairs \( K_d \) is defined as the number of pairs where \( x_i \) and \( x_j \) are in different orders. The Kendall’s Tau distance similarity is given by the formula:

\[
KTDS(X, Y) = 1 - \frac{2 \cdot K_d}{n(n-1)}
\]

where \( n \) is the total number of items.
Figure 7: Visualization of year distribution in READOC. (a) Document publication years of READOC-arXiv. (b) Document publication years of READOC-GitHub.

<table>
  <tr>
    <th>Parameter</th>
    <th>Value</th>
  </tr>
  <tr>
    <td>top_p</td>
    <td>0.8</td>
  </tr>
  <tr>
    <td>top_k</td>
    <td>100</td>
  </tr>
  <tr>
    <td>temperature</td>
    <td>0.7</td>
  </tr>
  <tr>
    <td>do_sample</td>
    <td>True</td>
  </tr>
  <tr>
    <td>repetition_penalty</td>
    <td>1.05</td>
  </tr>
</table>

Table 11: Generation parameters of Vision-Language Models.

D  Details of Experiments

D.1  Implementation of VLMs
This section primarily supplements the evaluation process of Vision-Language Models. Specifically, we adopt a one-page image-to-Markdown approach, concatenating the Markdown text generated for each page. The prompts we use are illustrated in Figure 12, and we uniformly apply the generation parameters for all VLMs as shown in Table 11.

D.2  Additional Case Study
We provide two additional case analyses, as shown in Figures 13 and 14. It is evident that none of the DSE systems demonstrated satisfactory performance, as they exhibited various types of errors, including Missing Content, Misidentifying Headings, Inaccurate Text Extraction, Confused Reading Order, Hallucinations, and Repetitions.
Figure 8: Visualization of year distribution in READOC-arXiv. (a) Document page counts of READOC-arXiv. (b) Document depth (heading levels) of READOC-arXiv. (b) Document Length (token counts) of READOC-arXiv.

Figure 9: Visualization of year distribution in READOC-GitHub. (a) Document page counts of READOC-GitHub. (b) Document depth (heading levels) of READOC-GitHub. (b) Document Length (token counts) of READOC-GitHub.

<table>
  <tr>
    <th>Headings</th>
    <td>re.compile(r'^([#1,6]) +(.)', re.MULTILINE)</td>
  </tr>
  <tr>
    <th>Embedded Formulas</th>
    <td>re.compile(r"\\((.*?)(?<!\\))\\")</td>
  </tr>
  <tr>
    <th>Isolated Formulas</th>
    <td>re.compile(r"\\((.+?)(?<!\\))", re.S)</td>
  </tr>
  <tr>
    <th>Tables</th>
    <td>re.compile(r"\n\begin{table}\{(?:\n\begin{table}\)*?\}\end{table}\n", re.S)</td>
  </tr>
  <tr>
    <th>Plain Text</th>
    <td>(The remaining part is treated as plain text)</td>
  </tr>
</table>

Figure 10: Document types of READOC-arXiv.

Figure 11: Segmentation module’s regular expressions.
Prompt for READoc-arXiv
This image displays a document page, convert the page content into Markdown format. Use continuous # to denote headings at each level. Display tables following markdown or latex format. Use $ or \( \) to surround inline math, and use $$ or \( \) to surround isolated math block. Don't explain, directly output the Markdown-format content.

Prompt for READoc-GitHub
This image displays a document page, convert the page content into Markdown format. Use continuous # to denote headings at each level. If it's a blank page, don't output anything. Otherwise, directly output the Markdown-format content without explanation.

Figure 12: Prompts of Vision-Language Models.
READDoc Ground Truth

starting configuration Beckett's text explicitly notes that every possible non-empty subset of characters appear on stage together at least once but admits that those subsets do not appear a uniform number of times [1].
If each subset of characters appears precisely once, then the series of subsets - represented by their binary words - would form a "universal code" (Vigna 2008). Gray codes of binary sets of bit words are equivalent to Hamilton paths in cycles in the \(V(n)\)-dimensional cube \(Q_n\). We feel this note provides the necessary background to this work and a more detailed account of the history and research we see [5].

#2 Preliminaries
The existence of a Beckett-Gray code is equivalent to being able to realize every subset of an \(n\)-set exactly once as successive states of a queue. The possible states are the bit positions that correspond to 1 s in Table 1, and the cycle is the non-cyclic Beckett-Gray code and the corresponding queue states.

<table>
  <tr><th>Code</th><th>Queue</th><th>Bit Positions</th></tr>
  <tr><td>0000</td><td> </td><td>[1]</td></tr>
  <tr><td>0001</td><td>|</td><td>[1,2]</td></tr>
  <tr><td>0010</td><td>||</td><td>[1,3]</td></tr>
  <tr><td>0011</td><td'||</td><td>[1,4]</td></tr>
  <tr><td>0100</td><td>|||</td><td>[2,3]</td></tr>
  <tr><td>0101</td><td>'||'</td><td>[2,4]</td></tr>
  <tr><td>0110</td><td>'|||'</td><td>[3,4]</td></tr>
  <tr><td>0111</td><td>''|''</td><td>[1,2,3]</td></tr>
  <tr><td>1000</td><td>'|||'</td><td>[1,2,4]</td></tr>
  <tr><td>1001</td><td>''||'</td><td>[1,3,4]</td></tr>
  <tr><td>1010</td><td>''|''</td><td>[2,3,4]</td></tr>
  <tr><td>1011</td><td>''|''</td><td>[1,2,3,4]</td></tr>
</table>

Table 1: A 3-bit non-cyclic binary Beckett-Gray code.

Two Gray codes are called isomorphic, ... Permuting bit positions preserves the Beckett-Gray ...

Figure 13: A case study from READDoc arXiv.

READDoc Ground Truth
- https://github.com/mcquarry/pendom
- https://github.com/ContextLogic/eventmaster
- https://github.com/... (Python/pandas backend)

## Query API

Example timeseries request
```javascript
{
    "panelist": 1,
    "range": {
        "from": "2016-10-31T06:33:44.866Z",
        "to": "2016-10-31T12:33:44.866Z",
        "raw": true
    },
    "target": [
        ["now-6h", "target_1", "type": "timeserie"],
        ["now-h", "metric_1"]
    ],
    "metrics": {
        "interval": "30s",
        "retention": 30000,
        "targets": [
            {"target": "upper_50","refId": "A", "type": "timeserie"},
            {"target": "upper_75","refId": "B", "type": "timeserie"}
        ]
    }
}

Example timeseries response
```javascript
[
    {
        "target": "upper_50", // The field being queried for "kpi_metrics",
        "values": [
            [1459614000, 365.2],
            [1459614200, 364.8],
            ...
        ]
    },
    {
        "target": "upper_75", // The field being queried for "datatools"
        "values": [
            [1459614000, 365.0],
            [1459614200, 364.8],
            ...
        ]
    }
]
GPT-4o-mini : Misidentifying Heading
# Starting Configuration
Beckett's text explicitly notes that every possible non-empty subset of characters appear on stage together at least once but admits that these subsets do not appear a uniform number of times [1].
...

InternVL-Chat-V1.5 : Missing Content
# 2 Preliminaries
The existence of a Beckett-Gray code is equivalent to being able to realize every subset of an n-set exactly once as successive states of a queue, where queue entries are the bit positions that correspond to 1 s in Table 1, and the cycle is the non-cyclic Beckett-Gray code and the corresponding queue states.

<table>
  <tr><th>Code</th><th>Queue</th><th>Bit Positions</th></tr>
  <tr><td>0000</td><td> </td><td>[1]</td></tr>
  <tr><td>0001</td><td>|</td><td>[1,2]</td></tr>
  <tr><td>0010</td><td>||</td><td>[1,3]</td></tr>
  <tr><td>0011</td><td'||</td><td>[1,4]</td></tr>
  <tr><td>0100</td><td>|||</td><td>[2,3]</td></tr>
  <tr><td>0101</td><td>'||'</td><td>[2,4]</td></tr>
  <tr><td>0110</td><td>'|||'</td><td>[3,4]</td></tr>
  <tr><td>0111</td><td>''|''</td><td>[1,2,3]</td></tr>
  <tr><td>1000</td><td>'|||'</td><td>[1,2,4]</td></tr>
  <tr><td>1001</td><td>''||'</td><td>[1,3,4]</td></tr>
  <tr><td>1010</td><td>''|''</td><td>[2,3,4]</td></tr>
  <tr><td>1011</td><td>''|''</td><td>[1,2,3,4]</td></tr>
</table>

Table 1: A 3-bit non-cyclic binary Beckett-Gray code.

Two Gray codes are called _isomorphic_ ... Permuting bit positions preserves the Beckett-Gray ...

<table>

Figure 14: A case study from READDoc GitHub.

Reviewer paper summaries: This paper introduces a novel benchmark called READoc, along with an evaluation framework named S3uite, designed for end-to-end document structure extraction. Additionally, the study evaluates the performance of several approaches on this benchmark.

The paper is about defining a novel task called Document Structured Extraction (DSE). The research includes new data collected automatically from Arxiv, Zenodo, and GitHub, and it also provides a benchmark and the model analysis in DSE.
Summary of strengths: 1.	The paper is easy to follow.
2.	S3uite metric proposed looks sound for measure for evaluating document structure extraction.

- new task comprising many challenges within extracting document information with a few subtasks;
- new dedicated dataset (multilingual one), and its comparison with other partial datasets;
- benchmarks with deep analysis: many new LLMs, and VLMs; analysis of how the lengths of the documents influence models' performance; and the same with the hierarchy of headings
Summary of weaknesses: 1.	The dataset scenarios presented are overly simplistic and far less rich than those in OmniDocBench. Particularly concerning is READOC-GitHub, as these README files are originally streamlined Markdown documents by nature. It is questionable whether they can adequately reflect real-world scenarios.
2.	There is insufficient testing of advanced models. As a benchmark for document structured extraction, it fails to evaluate advanced specialized models such as GOT and KOSMOS-2.5, as well as state-of-the-art general multimodal large models like Qwen2.5-VL and InternVL2.5.
3.	The claim that "Reading Order Detection is a relatively easy capability to acquire" seems rather optimistic, to say the least. After all, if Table 5 shows up on page 6 while Table 4 is hiding on page 7, one might wonder if even the authors struggled to decide the "correct" reading order themselves!
4.	The benchmark lacks effective visualization, making it difficult to fully understand its representativeness and diversity.

- I cannot understand the difference between VRDU (visually rich document understanding task) and the defined DSE. In task definition, it should be clearly compared with related tasks.
- The datasets are not compared in terms of their sizes and how long the documents are.
Comments, suggestions and typos: The authors need to clarify the distinctions between the benchmark proposed in this paper and existing benchmarks. Additionally, they should provide a more comprehensive demonstration of the diversity and representativeness of the proposed benchmark, along with performance evaluations of a broader range of advanced models.

In weak points, I depicted two issues that should be explained.
Other minor  issues:
- shortcuts such as VLMs, DocAI, and RAG can be introduced at the very first mention in  the text,
- line 111: Table ???
- I cannot access this GitHub in line 096: "The code and data are publicly available at https://github.com/XXX/XXX."
- In Conclusions, there is a reference to Figure 5, which is in the appendix.
Overall assessment scores: 2.5

4
Metareview score: 2.5
Metareview: This paper addresses the recognized need for better evaluation methods in Document Structure Extraction. Current benchmarks are often fragmented. The authors propose READoc, a new benchmark aiming for unified and realistic evaluation. They define the DSE task as converting complete, unstructured PDF documents into structured, semantically meaningful Markdown format. Alongside the dataset, they introduce the S3uite evaluation framework designed for this PDF-to-Markdown task. The paper uses this benchmark to evaluate several existing DSE approaches, identifying current performance gaps and highlighting challenges like handling multi-page documents and complex layouts.
Summary of reasons to publish: 1. The lack of unified, realistic benchmarks for end-to-end DSE is a known problem. This work directly tackles this issue, which is valuable for the community.
2. Defining DSE as a PDF-to-Markdown conversion task for complete documents is an interesting and potentially useful paradigm (praised by R-vJZd, R-WMZ9). It promotes holistic evaluation.
3. The READoc dataset, gathered from diverse real-world sources, is a substantial contribution. The authors provided data during rebuttal clarifying its scale compared to others (addressing R-WMZ9) and its multi-source nature (arXiv, GitHub, Zenodo).
4. The S3uite evaluation methodology appears logical for the defined task.