Result Details
AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization
Hradiš Michal, Ing., Ph.D., UAMT (FEEC), DCGM (FIT)
Dvořáková Martina
Jiroušek Václav
Kersch Filip
We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the late 19th and early 20th centuries.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.
Dataset; Non-Textual Elements; Graphical Elements; Documents
@inproceedings{BUT197672,
author="Martin {Kišš} and Michal {Hradiš} and Martina {Dvořáková} and {} and {}",
title="AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization",
booktitle="Document Analysis and Recognition – ICDAR 2025 Workshops",
year="2026",
pages="50--66",
publisher="Springer Nature Switzerland",
address="Cham",
doi="10.1007/978-3-032-09371-4\{_}4",
isbn="978-3-032-09370-7",
url="https://link.springer.com/chapter/10.1007/978-3-032-09371-4_4"
}