AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization
This dataset supports research in document layout analysis and object detection, primarily for historical documents in Czech and German, but it is incremental as it builds on existing datasets and methodologies.
The authors tackled the problem of document layout analysis by introducing the AnnoPage Dataset, a collection of 7,550 historical document pages with fine-grained annotations for 25 categories of non-textual elements, and provided baseline results using YOLO and DETR object detectors.
We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the late 19th and early 20th centuries.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.