AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization
The result's identifiers
Result code in 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>
Alternative codes found
RIV/67985971:_____/26:00645444 RIV/00094943:_____/26:N0000001 RIV/00023221:_____/25:N0000030
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization
Original language description
We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the late 19th and early 20th centuries.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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/DH23P03OVV033" target="_blank" >DH23P03OVV033: Orbis Pictus – book revival for cultural and creative sectors</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Document Analysis and Recognition – ICDAR 2025 Workshops
ISBN
978-3-032-09370-7
ISSN
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e-ISSN
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Number of pages
17
Pages from-to
50-66
Publisher name
Springer Nature Switzerland
Place of publication
Cham
Event location
Wuhan, Čína
Event date
Sep 16, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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