TextBite: A Historical Czech Document Dataset for Logical Page Segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197678" target="_blank" >RIV/00216305:26230/26:0197678 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-032-09368-4_8" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-032-09368-4_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-032-09368-4_8" target="_blank" >10.1007/978-3-032-09368-4_8</a>
Alternative languages
Result language
angličtina
Original language name
TextBite: A Historical Czech Document Dataset for Logical Page Segmentation
Original language description
Logical page segmentation is an important step in document analysis, enabling better semantic representations, information retrieval, and text understanding. Previous approaches define logical segmenta- tion either through text or geometric objects, relying on OCR or precise geometry. To avoid the need for OCR, we define the task purely as seg- mentation in the image domain. Furthermore, to ensure the evaluation remains unaffected by geometrical variations that do not impact text segmentation, we propose to use only foreground text pixels in the eval- uation metric and disregard all background pixels. To support research in logical document segmentation, we introduce TextBite, a dataset of historical Czech documents spanning the 18th to 20th centuries, fea- turing diverse layouts from newspapers, dictionaries, and handwritten records. The dataset comprises 8,449 page images with 78,863 annotated segments of logically and thematically coherent text. We propose a set of baseline methods combining text region detection and relation predic- tion. The dataset, baselines and evaluation framework can be accessed at https://github.com/DCGM/textbite-dataset.
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/DH23P03OVV060" target="_blank" >DH23P03OVV060: semANT - Semantic Document Exploration</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
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
978-3-032-09367-7
ISSN
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e-ISSN
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Number of pages
17
Pages from-to
124-140
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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