# Layout Analysis Dataset with SegmOnto

Thibault Clérice, Juliette Janes, Hugo Scheithauer, Sarah Bénière, Laurent Romary, Benoît Sagot

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## To cite this version:

Thibault Clérice, Juliette Janes, Hugo Scheithauer, Sarah Bénière, Laurent Romary, et al. Layout Analysis Dataset with SegmOnto. DH2024 - Annual conference of the Alliance of Digital Humanities Organizations, ADHO, Aug 2024, Washington DC, United States. hal-04513725

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DH2024: "Reinvention & Responsibility"

Layout Analysis Dataset with SegmOnto

Thibault Clérice¹, Juliette Janès¹, Hugo Scheithauer¹, Sarah Bénière¹,
Laurent Romary¹, and Benoît Sagot¹

¹ALMAnaCH - Automatic Language Modelling and Analysis &
Computational Humanities, Inria, Paris, France

November 2023

Automatic Text Recognition (ATR) has had a profound impact by generating extensive digital corpora from texts predating the digital era (Camps et al. 2023). While these technologies are undeniably valuable for tasks like human reading, PDF searching, plain-text keyword spotting, and, to a lesser extent, concordance building, the insufficient curation applied to most digitized texts (e.g., Archive.org, Project Gutenberg) presents a significant challenge when the correct sequential order of the text is compromised. Misidentifying footnotes, running titles, or marginal text as the main body of the text introduces noise into the character masses.

In computer vision, Document Layout Analysis (DLA) or Document Layout Seg-
mentation (DLS) aims to extract semantics from distinct sections within documents,
identifying common types like "text block" or "table." Recently, DLA has gained
traction in digital humanities projects seeking to move beyond text mining towards
document production (Scheithauer et al. 2021). In 2021, Gabay, Camps, and Pinche
2021 introduced the SegmOnto vocabulary, offering a controlled syntax to describe
document features across various types (printed, manuscripts, incunabula). With cat-
egories such as **MainZone** for main text bodies and distinctions like **MarginTextZone** or **RunningTitleZone**, SegmOnto facilitates contamination-free bag-of-words gener-
ation, considering non-textual elements like stamps and digitization artifacts. While
widely used in diverse projects, from medieval manuscripts (Pinche 2022) to printed
classical texts commentaries (Najem-Meyer and Romanello 2022), SegmOnto remains
relatively high-level and extendable. Its three-part syntax includes a controlled first
level (e.g., **MainZone**) but allows flexibility in secondary (subtype) and third (num-
bering) levels, offering suggestions like **MainZone:Column#A**.
In this paper, we showcase an extensive implementation of the SegmOnto vocab-
ulary for Layout Analysis, driven by a three-fold objective:

* Advocating for the adoption of a TEI-Based second level of SegmOnto, concentrating on segmenting sub-blocks of content (paragraphs, lists, etc.) when applied to text;
* Facilitating the generation of large bodies of digital texts that adhere to the sequential nature of the original content, despite its nonsequential representation on paper;
* Charting the course for more text-centric pipelines by recognizing that certain contents demand additional post-processing, such as bibliographic or lexicon entries, tables, etc.

This implementation is embodied in the “Layout Analysis Dataset with SegmOnto” (LADaS), a highly diverse dataset containing 3300 manually annotated and cross-corrected digitized page images sourced from 2000 distinct documents. The dataset encapsulates a broad chronological range, spanning from the 17th century to the 21st, and encompasses a wide array of genres, including poetry, academic papers (both humanities and STEM), novels, plays, epistolary works, and government and law reports.

This abstract begins by examining the current status of prominent datasets in
the field of DLA. It then proceeds to discuss the guidelines, including our additions
to the SegmOnto ontology. Following this, we present details about our dataset and
its various components. Lastly, we conduct an evaluation to assess its effectiveness,
demonstrated through its application in training an object detection model.

# 1 Current datasets

In the realm of Layout Analysis, datasets play a pivotal role, and recent develop-
ments have seen the establishment of benchmarking datasets for Computer Vision
(CV) applications. Datasets like PubLayNet (Zhong, Tang, and Yepes 2019) and
DocBank (Li et al. 2020) stand out, primarily centering around scientific papers
sourced from ArXiv or PubMed. Newer datasets, such as DocLayNet (Pfitzmann
et al. 2022) and M6Doc (Cheng et al. 2023), broaden their scope to include a more
diverse range of sources. This last dataset appears most similar to LADaS, with
akin labels, but differing in depth and focusing solely on modern documents, with a
significant focus put on scientific papers due to their prevalence in LaTeX formats.
This format facilitates preprocessing, enabling the extraction of valuable visual clas-
sification information (Pfitzmann et al. 2022).
<table><caption>Table 1: Comparison of Layout Analysis Datasets with Provided Page and Label Counts.</caption><thead><tr><th>Dataset</th><th>Documents Type</th><th>Pages</th><th>Labels</th></tr></thead><tbody><tr><td>PubLayNet (Zhong, Tang, and Yepes 2019)</td><td>Medical papers</td><td>360000</td><td>5</td></tr><tr><td>DocBank (Li et al. 2020)</td><td>STEM papers</td><td>50000</td><td>12</td></tr><tr><td>DocLayNet (Pfitzmann et al. 2022)</td><td>Various Modern Documents</td><td>80863</td><td>11</td></tr><tr><td>Scibank (Grijalva et al. 2022)</td><td>STEM papers</td><td>74435</td><td>12</td></tr><tr><td>PrimA (Antonacopoulos et al. 2009)</td><td>Various Modern Documents</td><td>1240</td><td>10</td></tr><tr><td>M6Doc (Cheng et al. 2023)</td><td>Various Modern Documents</td><td>9080</td><td>74</td></tr><tr><td>SCUT CAB (Hiuyi et al. 2023)</td><td>Chinese Ancient Books</td><td>4000</td><td>27</td></tr><tr><td>American Stories (Dell et al. 2023)</td><td>Historic Press</td><td>2200</td><td>7</td></tr><tr><td>HJD (Shen, Zhang, and Dell 2020)</td><td>19th-20th Japanese Documents</td><td>2271</td><td>7</td></tr><tr><td>Gallicorpora (Sagot et al. 2022)</td><td>Literary books and manuscripts</td><td>981</td><td>15</td></tr><tr><td>HORAE (Boillet et al. 2019)</td><td>Books of hours</td><td>500</td><td>13</td></tr><tr><td>Ajax Multicommentary (Romanello, Sven, and Robertson 2021)</td><td>19th Critical Editions</td><td>300</td><td>18</td></tr></tbody></table>

The diverse sources in layout analysis datasets often result in generic and numerous annotations using custom labels, with some datasets employing a generic approach, while others provide detailed zone descriptions, sometimes leading to more than to 70 labels (cf. Table 1). Materials commonly encountered in Digital Humanities (DH) endeavors, including historical press, monographs, manuscripts, and critical editions, have been addressed across different projects. In projects conducted after the introduction of SegmOnto, it has been utilized with a focus on standardization. However, both DH and CV projects typically lack the provision of a cross-genre, diachronic corpus aimed at text structuration.

## 2 Guidelines

While SegmOnto offers a set of zone types for distinguishing noise from the main body of text, it lacks specifications regarding the scope of annotation (e.g., whether the main zone should apply to the entire column or each paragraph individually) and lacks tools for normalized classification of sub-elements within the first level (e.g., paragraphs, lists). To construct our guidelines and class set, we selected subclasses based on the availability of a corresponding TEI class, the visual distinguishability of an element, and the relevance for post-processing information separation (e.g., GraphiZone:Legend and GraphicZone).

The most comprehensive main category in our subset is the MainZone, aligning with our primary textual focus. We distinguish groups of lines (⟨lg⟩), paragraphs (⟨p⟩), lists (⟨item⟩ within a ⟨list⟩), and headings (⟨title⟩). Although headings may resemble SegmOnto's HeadingLine, our application of zones to smaller text portions necessitates their representation. For many of these categories, as well as MarginTextZone:Notes, a third level marks the continuation of an element either
in a previous page or column, when this can be concluded from the layout (see Figure 1).

Finally, for the **TableZone** and **GraphicZone**, their sublevel **Legend** are instrumental in providing context for tabular and graphical elements. It enhances their interpretability. Furthermore, our annotations encompass specific subclasses such as **GraphicZone:P**, which focuses on annotating textual content within graphical elements. Notably, our design emphasizes the necessity for the main classes, such as **GraphicZone** and **TableZone**, to overlap with the legend subclasses. This strategic approach ensures a nuanced representation of the intricate relationships between describing textual content and graphical components, contributing to a more comprehensive understanding of document layout and structure in our dataset (see the complete set of classes in appendix, Table 5).

<figure><img src="image_3.png" /><figcaption>Figure 1: Example of Continued third level used, for a margin text note that continues on a second column. The lack of indentation marks the continuation here.</figcaption></figure>

## 3 Content

Our dataset comprises five subsets (*cf*. Table 2, Figure 2 for its temporal coverage and Figure 4 for sample images), with annotations primarily carried out in an iterative
process on the Roboflow platform (Dwyer, Nelson, Solawetz, et al. 2022), involving annotation, training, and correction. Four distinct annotators contributed to the data annotations, and all annotations underwent proofreading by at least one additional annotator other than the original corrector. The object detection models were developed using the YOLOv8 architecture and associated software, subsequently integrated for in-app utilization on Roboflow. The final dataset is overly biased towards prose and paragraphs (see Table 5.)

**The Gallica Monographies subset** gathers 1785 books from the *Bibliothèque nationale de France* (BnF) and specifically from the OCR dump they provide on their data platform (*Gallica: bibliothèque numérique* n.d.). It contains various genres but it is mostly centered around genres containing paragraphs and has very few plays and poetry.

**The “DataCatalogue” subset** comprises 713 auction sales catalogs housed in the BnF and the Institut national d'histoire de l'art (INHA). These printed publications offer detailed information about auctioned items (numismatic, books, antiques, artworks, furniture, and luxury items), presenting structured entries followed by illustrations with distinctive layouts.

**The Persée subset** is derived from the French academic digital library and was curated by randomly selecting images from documents described on their OAI PMH server. Encompassing the period from 1824 to 2015, it includes diverse academic (STEM or HASS) papers and *miscellanea*. The layouts are very diverse, featuring unique topologies.

**The Fingers subset** has been built around the idea of adding noise in the dataset, we poorly digitized books using a book scanner but leaving fingers, background, and bent pages in the shot of the camera. These are meant to replicate more on-site issues and provide the ability for models to answer different needs, including the need of a researcher or student at a library to extract the text of a book.

*Miscellanea* are books or series of interest to the authors.

# 4 Evaluation

To assess the potential of our data, we trained an object detection model utilizing YOLOv8l with an 85/10/5% split and data augmentation on the training set (cf.
<figure><img src="image_4.png" /><figcaption>Figure 2: Corpus distribution over years for the main subsets. Subsets' graph uses the percentage of each subset</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3: Presence of classes across time on the *Monographies* subset, which shows an important bias toward *MainZone:P RunningTitleZone*, and *MarginTextZone:Notes* with a constant presence in the corpus.</figcaption></figure>
<table><caption>Table 2: LADaS Subsets properties.</caption><thead><tr><th>Subset</th><th>Genre</th><th>Annotated pages</th><th>Random</th><th>Max. samples per doc.</th><th>Century Start</th><th>Century End</th><th>Presence of other scripts than Latin</th></tr></thead><tbody><tr><td>Monographies</td><td>Misc.</td><td>1991</td><td>✓</td><td>1</td><td>17</td><td>21</td><td></td></tr><tr><td>DataCatalogue</td><td>Catalogs</td><td>750</td><td>✓</td><td>2</td><td>18</td><td>21</td><td></td></tr><tr><td>Persée</td><td>Academic</td><td>815</td><td>✓</td><td>1</td><td>19</td><td>21</td><td>✓</td></tr><tr><td>Fingers</td><td>Misc.</td><td>60</td><td>✓</td><td>2</td><td>20</td><td>21</td><td></td></tr><tr><td>Miscallanea</td><td>Misc.</td><td>8</td><td></td><td>1</td><td>19</td><td>19</td><td>✓</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Figure 4: Example images from subsets.</figcaption></figure>

The model<fnref n="1" /> provides a strong score and shows an ability to detect most kinds of elements quite efficiently, except for rare items in the corpus, such as <sp>, <lg> and <list> (see Table 3). The third SegmOnto level which we use for element continuation (e.g. MainZone:P#continued) gets mostly captured, or at worst confused with the non-continuation equivalent (see Figure 5). The overall score (61.9 mAP50) paints a good picture and also provides promises for a bigger dataset in the future.

## 5 Conclusion

This paper presents two pivotal contributions to our community: (1) a set of SegmOnto guidelines that elevates text structuration beyond mere denoising of sequential data and (2) an initial set of images suitable for benchmarking architectures or training models for data extraction in extensive document collections. While a

<footnote n="1">¹A demo of it used in a pipeline at https://defi-colaf.github.io/TEIminatOCR/.</footnote>
<table><caption>Table 3: Scores on the test set. We put in bold the categories that are both useful for text extraction and getting good scores. We can see that <sp>, <lg>, and <list> are yet to be well recognized but we expect these scores to be driven by the rarity of those classes in the training set.</caption><thead><tr><th>Class</th><th>Images</th><th>Instances</th><th>Box(P)</th><th>Box(R)</th><th>mAP50</th><th>mAP50-95</th></tr></thead><tbody><tr><td>all</td><td>288</td><td>2516</td><td>0.602</td><td>0.612</td><td>0.619</td><td>0.414</td></tr><tr><td>DigitizationArtefactZone</td><td>288</td><td>9</td><td>0.59</td><td>0.778</td><td>0.714</td><td>0.417</td></tr><tr><td>DropCapitalZone</td><td>288</td><td>7</td><td>0.749</td><td>0.852</td><td>0.798</td><td>0.443</td></tr><tr><td>GraphicZone</td><td>288</td><td>30</td><td>0.721</td><td>0.633</td><td>0.696</td><td>0.548</td></tr><tr><td>GraphicZone:Decoration</td><td>288</td><td>18</td><td>0.748</td><td>0.659</td><td>0.746</td><td>0.344</td></tr><tr><td>GraphicZone:Legend</td><td>288</td><td>25</td><td>0.694</td><td>0.4</td><td>0.461</td><td>0.274</td></tr><tr><td>GraphicZone:Maths</td><td>288</td><td>17</td><td>0.268</td><td>0.412</td><td>0.31</td><td>0.209</td></tr><tr><td>MainZone:Date</td><td>288</td><td>7</td><td>0.682</td><td>0.714</td><td>0.765</td><td>0.357</td></tr><tr><td>MainZone:Entry</td><td>288</td><td>393</td><td>0.874</td><td>0.888</td><td>0.909</td><td>0.695</td></tr><tr><td>MainZone:Entry#Continued</td><td>288</td><td>6</td><td>1</td><td>0.366</td><td>0.588</td><td>0.379</td></tr><tr><td>MainZone:Form</td><td>288</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>MainZone:Head</td><td>288</td><td>274</td><td>0.738</td><td>0.77</td><td>0.79</td><td>0.525</td></tr><tr><td>MainZone:Lg</td><td>288</td><td>6</td><td>0.619</td><td>1</td><td>0.901</td><td>0.728</td></tr><tr><td>MainZone:List</td><td>288</td><td>97</td><td>0.498</td><td>0.412</td><td>0.381</td><td>0.258</td></tr><tr><td>MainZone:Other</td><td>288</td><td>22</td><td>0.352</td><td>0.273</td><td>0.204</td><td>0.0952</td></tr><tr><td>MainZone:P</td><td>288</td><td>900</td><td>0.822</td><td>0.92</td><td>0.895</td><td>0.787</td></tr><tr><td>MainZone:P#Continued</td><td>288</td><td>98</td><td>0.744</td><td>0.847</td><td>0.842</td><td>0.773</td></tr><tr><td>MainZone:Signature</td><td>288</td><td>62</td><td>0.676</td><td>0.726</td><td>0.731</td><td>0.402</td></tr><tr><td>MainZone:Sp</td><td>288</td><td>26</td><td>0.713</td><td>0.192</td><td>0.339</td><td>0.241</td></tr><tr><td>MainZone:Sp#Continued</td><td>288</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>MarginTextZone:ManuscriptAddendum</td><td>288</td><td>10</td><td>0.75</td><td>0.899</td><td>0.855</td><td>0.419</td></tr><tr><td>MarginTextZone:Notes</td><td>288</td><td>73</td><td>0.533</td><td>0.616</td><td>0.608</td><td>0.424</td></tr><tr><td>MarginTextZone:Notes#Continued</td><td>288</td><td>2</td><td>0</td><td>0</td><td>0.108</td><td>0.097</td></tr><tr><td>NumberingZone</td><td>288</td><td>214</td><td>0.878</td><td>0.93</td><td>0.93</td><td>0.45</td></tr><tr><td>PageTitleZone</td><td>288</td><td>6</td><td>0.553</td><td>1</td><td>0.876</td><td>0.665</td></tr><tr><td>PageTitleZone:Index</td><td>288</td><td>11</td><td>0.681</td><td>0.818</td><td>0.787</td><td>0.677</td></tr><tr><td>QuireMarkZone</td><td>288</td><td>10</td><td>0.308</td><td>0.5</td><td>0.418</td><td>0.201</td></tr><tr><td>RunningTitleZone</td><td>288</td><td>141</td><td>0.858</td><td>0.904</td><td>0.888</td><td>0.532</td></tr><tr><td>StampZone</td><td>288</td><td>14</td><td>0.704</td><td>0.714</td><td>0.757</td><td>0.566</td></tr><tr><td>StampZone:Sticker</td><td>288</td><td>7</td><td>0.558</td><td>0.714</td><td>0.765</td><td>0.54</td></tr><tr><td>TableZone</td><td>288</td><td>23</td><td>0.77</td><td>0.783</td><td>0.698</td><td>0.523</td></tr><tr><td>TableZone:Legend</td><td>288</td><td>6</td><td>0.582</td><td>0.249</td><td>0.417</td><td>0.259</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>Figure 5: Confusion matrix, “background” means that the object was not detected. Most inter-classes confusion happens between items with a third level (e.g. MainZone:Entry and MainZone:Entry#Continued, as well as between main parts of the text: P, Other, List, and SP, with P being the class they are confused with.</figcaption></figure>
comprehensive description of the guidelines is anticipated in the upcoming year, this
paper offers a succinct preview of their potential and performance, underscored by a
modest 3000 training samples.

However, our current project's focus is primarily on documents written in the Latin script and printed in Western Europe. While this limitation is acknowledged, we aim to overcome it through the refinement of guidelines, applying them to various printing traditions, and fostering collaboration with researchers specializing in diverse practices. This proactive approach ensures a more robust and inclusive framework for text structuration, paving the way for broader applications across different linguistic and printing contexts.
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Zhong, Xu, Jianbin Tang, and Antonio Jimeno Yepes (Sept. 2019). "PubLayNet: largest dataset ever for document layout analysis". In: 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 1015-1022. DOI: 10.1109/ICDAR.2019.00166. URL: https://github.com/ibm-aur-nlp/PubLayNet.
<table><caption>Table 4: Detailed Comparison of Layout Analysis Datasets (Table 1)</caption><thead><tr><th>Dataset</th><th>Type of documents</th><th>Digitization</th><th>Pages</th><th>Human annotators</th><th>Classes<br>Number</th><th>Examples</th><th>Reusable</th></tr></thead><tbody><tr><td>Scibank</td><td>STEM papers</td><td>No</td><td>74435</td><td>No</td><td>12</td><td>abstract, textblocks, caption, equation, inline equation, figure, keywords, reference, section, subsection, table, title.</td><td>CC-BY</td></tr><tr><td>PubLayNet</td><td>Medical papers</td><td>No</td><td>360000</td><td>No</td><td>5</td><td>Text, Title, List, Table, Figure</td><td>CDLA - Permis 1.0</td></tr><tr><td>M6Doc</td><td>Textbook, test paper, magazine, newspaper, scientific papers, notes, book</td><td>Mixed</td><td>9080</td><td>Yes</td><td>74</td><td>Head, SubHead, Headline, Title, Paragraph, Section, Section Title, Chapter Title, Footer, Footnote, Ordered List, Nonordered List, Table, Caption...</td><td>CC BY-NC-ND 4.0</td></tr><tr><td>PrimA</td><td>Magazine, technical journals, forms, bank statements, ads</td><td>Mixed</td><td>1240</td><td>Yes</td><td>10</td><td>Text, image, line drawing, graphic, table, chart, separator, maths, noise, frame</td><td>None</td></tr><tr><td>DocLayNet</td><td>financial reports, scientific articles, laws and regulations, government tenders, manuals, patents</td><td>Mixed</td><td>80863</td><td>Yes</td><td>11</td><td>Caption, Footnote, Formula, ListItem, Page Footer, Page Header, Picture, Section Header, Table, Text, Title</td><td>CDLA - Permis 1.0</td></tr><tr><td>DocBank</td><td>STEM papers</td><td>No</td><td>500000</td><td>No</td><td>12</td><td>Abstract, Author, Caption, Equation, Figure, Footer, List, Paragraph, Reference, Section, Table, Title</td><td>None</td></tr><tr><td>SCUT CAB</td><td>Chinese Books</td><td>Ancient</td><td>4000</td><td>Yes</td><td>27</td><td>bibliography, header, pagebox, book number, figure, author, title, ear note, chapter title...</td><td>CC-BY-NC-ND 4.0</td></tr><tr><td>Historial Japanese Dataset</td><td>Japanese Doc (19th-20th)</td><td>Historical</td><td>Yes</td><td>2271</td><td>7</td><td>page frame, row, title region, text region, title, subtitle, other</td><td>APACHE 2.0</td></tr><tr><td>Gallicorpora</td><td>Literary books &amp; manuscripts</td><td>Yes</td><td>981</td><td>Yes</td><td>15</td><td>SegmOnto (level 1)</td><td></td></tr><tr><td>American Stories</td><td>Historic Press</td><td>Yes</td><td>2200</td><td>Yes</td><td>7</td><td>Author, Article, Header, Headline, Table, Masthead, Cartoon/ads</td><td>CC-BY</td></tr><tr><td>HORAE</td><td>Books of hours</td><td>Yes</td><td>500</td><td>Yes</td><td>13</td><td>Page, textregion, bordertext, textline, miniature, decorated border, illustrated border, initiale (simple, decorated, historiated), line filer, music notation, ornamentation</td><td>CC-BY</td></tr><tr><td>AJAX Multicommentary Dataset</td><td>Critical editions 19th</td><td>Yes</td><td>300</td><td>Yes</td><td>18</td><td>Commentary, critical apparatus, footnotes, page number, text number, bibliography, handwritten marginalia, index, others, printed marginalia, table of contents, title, translation, appendix, introduction, preface, primary text, running header</td><td>CC-BY</td></tr></tbody></table>
# Training parameters

* 100 epochs

* Image Size: 960 pixels

* Image augmentation

- Outputs per training example: 3

- Rotation: Between -3° and +3°

- Shear: ±3° Horizontal, ±3° Vertical

- Exposure: Between -30% and +30%

- Blur: Up to 1.5px

- Noise: Up to 2% of pixels
<table><caption>Table 5: LADaS annotation schema. Cont. indicates that the class can have a third SegmOnto level indicating that the element extends from a preceding zone, e.g. MainZone:P#Continued.</caption><thead><tr><th>Label</th><th>Description</th><th>Cont.</th></tr></thead><tbody><tr><td>DigitalArtefactZone</td><td>Element added during the document's digitization</td><td></td></tr><tr><td>DropCapitalZone</td><td>Initial capital at the beginning of a text</td><td></td></tr><tr><td>GraphicZone:Decoration</td><td>Decorative image without text (banner, ornamental dividers, etc.)</td><td></td></tr><tr><td>GraphicZone</td><td>Illustration such as images, photographs, or graphs, and their legends</td><td></td></tr><tr><td>GraphicZone:Legend</td><td>Textual caption for a GraphicZone or a GraphicZone:Maths</td><td></td></tr><tr><td>GraphicZone:Maths</td><td>Mathematical formula not in a paragraph</td><td></td></tr><tr><td>GraphicZone:P</td><td>Paragraph in a GraphicZone</td><td></td></tr><tr><td>MainZone:Date</td><td>Date isolated from the rest of a paragraph, at the beginning or the end</td><td></td></tr><tr><td>MainZone:Entry</td><td>Structured text such as bibliographic and catalogs' entries</td><td>✓</td></tr><tr><td>MainZone:Form</td><td>Form to complete</td><td></td></tr><tr><td>MainZone:Head</td><td>Title of a part or a chapter on a non-title page</td><td></td></tr><tr><td>MainZone:List</td><td>Item of an enumerated or ordered list</td><td>✓</td></tr><tr><td>MainZone:Lg</td><td>Group of verse lines</td><td>✓</td></tr><tr><td>MainZone:Other</td><td>When none of the previous labels can be used</td><td></td></tr><tr><td>MainZone:P</td><td>Paragraph</td><td>✓</td></tr><tr><td>MainZone:Signature</td><td>Name of a person visually separated from the rest of the text</td><td></td></tr><tr><td>MainZone:Sp</td><td>Performance text, including stage directions and name of the person speaking in case of a theatrical text</td><td>✓</td></tr><tr><td>MarginTextZone:ManuscriptAddendum</td><td>Handwritten note</td><td></td></tr><tr><td>MarginTextZone:Notes</td><td>Footnote and margin note</td><td>✓</td></tr><tr><td>NumberingZone</td><td>Page Numbering</td><td></td></tr><tr><td>PageTitleZone</td><td>Front Cover of the document</td><td></td></tr><tr><td>PageTitleZone:Index</td><td>Table of contents</td><td></td></tr><tr><td>QuiremarkZone</td><td>Information at the bottom of a page which is not page number or running title</td><td></td></tr><tr><td>RunningTitleZone</td><td>Title or abbreviated title at the top or the bottom of the page</td><td></td></tr><tr><td>StampZone</td><td>Library Stamp</td><td></td></tr><tr><td>StampZone:Sticker</td><td>Sticker on library books</td><td></td></tr><tr><td>TableZone</td><td>Table and its legend</td><td></td></tr><tr><td>TableZone:Legend</td><td>Textual caption for a TableZone</td><td></td></tr></tbody></table>