BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata Extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197676" target="_blank" >RIV/00216305:26230/26:0197676 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-032-04624-6_17" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-032-04624-6_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-04624-6_17" target="_blank" >10.1007/978-3-032-04624-6_17</a>

Result language

angličtina

Original language name

BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata Extraction

Original language description

Manual digitization of bibliographic metadata is time consuming and labor intensive, especially for historical and real-world archives with highly variable formatting across documents. Despite advances in machine learning, the absence of dedicated datasets for metadata extraction hinders automation. To address this gap, we introduce BiblioPage, a dataset of scanned title pages annotated with structured bibliographic metadata. The dataset consists of approximately 2,000 monograph title pages collected from 14 Czech libraries, spanning a wide range of publication periods, typographic styles, and layout structures. Each title page is annotated with 16 bibliographic attributes, including title, contributors, and publication metadata, along with precise positional information in the form of bounding boxes. To extract structured information from this dataset, we valuated object detection models such as YOLO and DETR combined with transformer-based OCR, achieving a maximum mAP of 52 and an F1 score of 59. Additionally, we assess the performance of various visual large language models, including LlamA 3.2-Vision and GPT-4o, with the best model reaching an F1 score of 67. BiblioPage serves as a real-world benchmark for bibliographic metadata extraction, contributing to document understanding, document question answering, and document information extraction.

Czech name

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Czech description

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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)

Project

<a href="/en/project/DH23P03OVV066" target="_blank" >DH23P03OVV066: Smart digiline - machine learning for digitization of printed heritage</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

Document Analysis and Recognition – ICDAR 2025

ISBN

978-3-032-04623-9

ISSN

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e-ISSN

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Number of pages

17

Pages from-to

287-304

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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