AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197672" target="_blank" >RIV/00216305:26230/26:0197672 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985971:_____/26:00645444 RIV/00094943:_____/26:N0000001 RIV/00023221:_____/25:N0000030

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-032-09371-4_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-032-09371-4_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-09371-4_4" target="_blank" >10.1007/978-3-032-09371-4_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization

Popis výsledku v původním jazyce

We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the late 19th and early 20th centuries. The dataset is designed to support research in document layout analysis and object detection. Each page is annotated with axis-aligned bounding boxes (AABB) representing elements of 25 categories of non-textual elements, such as images, maps, decorative elements, or charts, following the Czech Methodology of image document processing. The annotations were created by expert librarians to ensure accuracy and consistency. The dataset also incorporates pages from multiple, mainly historical, document datasets to enhance variability and maintain continuity. The dataset is divided into development and test subsets, with the test set carefully selected to maintain the category distribution. We provide baseline results using YOLO and DETR object detectors, offering a reference point for future research. The AnnoPage Dataset is publicly available on Zenodo (https://doi.org/10.5281/zenodo.12788419), along with ground-truth annotations in YOLO format.

Název v anglickém jazyce

AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization

Popis výsledku anglicky

We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the late 19th and early 20th centuries. The dataset is designed to support research in document layout analysis and object detection. Each page is annotated with axis-aligned bounding boxes (AABB) representing elements of 25 categories of non-textual elements, such as images, maps, decorative elements, or charts, following the Czech Methodology of image document processing. The annotations were created by expert librarians to ensure accuracy and consistency. The dataset also incorporates pages from multiple, mainly historical, document datasets to enhance variability and maintain continuity. The dataset is divided into development and test subsets, with the test set carefully selected to maintain the category distribution. We provide baseline results using YOLO and DETR object detectors, offering a reference point for future research. The AnnoPage Dataset is publicly available on Zenodo (https://doi.org/10.5281/zenodo.12788419), along with ground-truth annotations in YOLO format.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/DH23P03OVV033" target="_blank" >DH23P03OVV033: Orbis Pictus – oživení knihy pro kulturní a kreativní odvětví</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Document Analysis and Recognition – ICDAR 2025 Workshops

ISBN

978-3-032-09370-7

ISSN

—

e-ISSN

—

Počet stran výsledku

17

Strana od-do

50-66

Název nakladatele

Springer Nature Switzerland

Místo vydání

Cham

Místo konání akce

Wuhan, Čína

Datum konání akce

16. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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