=Paper=
{{Paper
|id=Vol-3937/short13
|storemode=property
|title=Digital Maktaba Project: Proposing a Metadata-Driven Framework for Arabic Library Digitization
|pdfUrl=https://ceur-ws.org/Vol-3937/short13.pdf
|volume=Vol-3937
|authors=Amina El Ganadi,Luca Gagliardelli,Sania Aftar,Federico Ruozzi
|dblpUrl=https://dblp.org/rec/conf/ircdl/GanadiGAR25
}}
==Digital Maktaba Project: Proposing a Metadata-Driven Framework for Arabic Library Digitization==
https://ceur-ws.org/Vol-3937/short13.pdf
Digital Maktaba Project: Proposing a Metadata-Driven
Framework for Arabic Library Digitization
Amina El Ganadi1,2,3,* , Luca Gagliardelli1 , Sania Aftar1 and Federico Ruozzi1,3
1
University of Modena and Reggio Emilia, Modena, Italy
2
University of Palermo, Palermo, Italy
3
Fondazione per le Scienze Religiose (FSCIRE)
Abstract
The rapid digitization of cultural heritage has underscored the critical need for robust digital libraries, particularly
for underrepresented languages like Arabic and Persian. This paper describes the methodologies and challenges
involved in developing a metadata-driven Arabic digital library, utilizing bibliographic metadata extracted from
the Diamond catalogue. It explores advanced metadata schemas, such as Dublin Core, and integrates text
recognition technologies and preservation strategies to address key concerns of accessibility, scholarly use, and
the long-term preservation of Arabic-script texts. The paper delves into specific challenges of processing Arabic
script, including handling calligraphy, diacritics, and ligatures, and introduces innovative solutions like the use of
frontispiece images to train OCR systems. Furthermore, it discusses how integrated metadata could not only
enhance text recognition but also improve user engagement by enabling refined search functionalities and better
resource discovery. Finally, the paper outlines future directions for expanding metadata frameworks to ensure
interoperability and the long-term preservation of cultural heritage.
Keywords
Document Analysis, Arabic Digital Library, Bibliographic Metadata, Digitization, Digital Maktaba Project, OCR,
Cultural Heritage, Natural Language Processing.
1. Introduction
In recent years, libraries have undergone a transformative shift toward digital environments, driven by
advances in artificial intelligence (AI), machine learning, and digitization technologies. This transfor-
mation is not merely a technological upgrade; it represents a fundamental rethinking of how cultural
heritage materials, scholarly works, and historical documents are preserved, accessed, and interpreted
in digital formats. For institutions holding large collections of Arabic and Islamicate texts, this shift
opens up new possibilities for enhanced discovery, interoperability, and user engagement.
Central to this digital evolution is the utilization of metadata as a core organizational principle.
Metadata, from bibliographic information to structured descriptive tags, creates the backbone of any
digital library system. Metadata provides key details about a resource such as author, title, publication
date, and subject matter, playing a vital role in cataloguing, discovery, and resource management.
Libraries have traditionally used standardized systems like MARC (Machine-Readable Cataloguing) and
Dublin Core to maintain consistency and interoperability across institutions [1]. These frameworks
form the foundation of efficient library operations, helping librarians and users navigate extensive
collections of both physical and digital resources.
With proper standardization and adherence to established library protocols, metadata allows for
scalable integration of diverse collections and seamless alignment with global digital library networks.
As libraries migrate to digital repositories, metadata management becomes essential not only for
IRCDL 2025: 21st Conference on Information and Research Sciences Connecting to Digital and Library Science, February 20-21
2025, Udine, Italy
*
Corresponding author.
$ amina.elganadi@unimore.it (A. El Ganadi); luca.gagliardelli@unimore.it (L. Gagliardelli); sania.aftar@unimore.it
(S. Aftar); federico.ruozzi@unimore.it (F. Ruozzi)
0000-0002-8196-2628 (A. El Ganadi); 0000-0001-5977-1078 (L. Gagliardelli); 0000-0001-8151-8941 (S. Aftar);
0000-0003-2729-5016 (F. Ruozzi)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
cataloguing but also for ensuring access, improving visibility, and fostering user engagement with
digital resources [2].
Alongside these foundational elements, technologies like Optical Character Recognition (OCR) and
AI-driven text analysis enable more accurate and accessible digital representations of physical materials.
However, developing OCR technologies for Arabic texts presents unique challenges due to the script’s
cursive nature, variable letter shapes, and presence of diacritics. Mansoor Alghamdi and William
Teahan [3] highlight that while handwritten Arabic text poses significant challenges for OCR systems,
printed Arabic text is also notably difficult. Arabic-script OCR programs often struggle with accuracy,
frequently failing to meet the high expectations set by their marketing claims [3].
Existing datasets, such as the Arabic Printed Text Image (APTI), have supported progress in OCR
research, but efforts remain fragmented and often neglect specialized materials like ornate printed title
pages. While advancements in deep learning and Vision-Language Models (VLMs) [4, 5] have improved
recognition capabilities, the absence of targeted datasets for frontispiece images continues to limit the
accuracy and efficiency of digital library cataloging systems.
This multifaceted approach, combining metadata extraction, standardized protocols, and cutting-edge
OCR methods, ensures both the integrity of the original texts and the creation of rich, searchable
interfaces that enhance the end-user experience. This article presents a methodology that prioritizes
metadata extraction from physical library catalogs, digitization of frontispiece images, and subsequent
enrichment using widely adopted standards. By referencing prominent case studies such as Europeana
and WorldCat 1 , and highlighting platforms like eScriptorium2 , we establish a framework that is both
theoretically grounded and practically tested, ultimately advancing the integration of Arabic texts into
the global digital information ecosystem.
Currently, this approach is being further demonstrated through a focused case study of the La Pira
Library in Palermo3 , showcasing its scalability and adaptability to other collections, including those
containing Arabic and Persian texts. As this work progresses, the convergence of well-structured
metadata, AI-assisted text analysis, and adherence to international standards continues to provide a
promising pathway toward the creation of comprehensive and user-friendly digital libraries. Ongoing
efforts aim to refine these methods, ensuring their effectiveness and broad applicability in diverse library
settings.
2. Related Work
Many researchers have explored the Semantic Web’s role in digital libraries, often focusing on theoretical
aspects without addressing practical implementation [6, 7, 8, 9]. Others have studied bibliographic
ontologies [10, 11, 12, 13] and AI-based document classification using similarity measures [14]. Some
Arabic researchers have combined NLP with automatic classification and ontology creation [15].
The integration of artificial intelligence (AI) in the domain of library and information science has
gained significant attention, with numerous studies investigating its applications, benefits, and asso-
ciated challenges [16, 17, 18, 19, 20, 21]. Farag et al. [22] investigated AI adoption in Saudi academic
libraries, finding limited staff understanding, with 69% not using AI. Despite its potential in indexing and
user support, adoption is hindered by inadequate training, limited infrastructure and technical expertise.
Hussain [23] examined AI integration in library services, noting its potential to enhance operations
despite barriers like budget constraints, librarian perceptions, and limited technical skills. The study
emphasizes affordable AI applications that can improve service delivery and drive library development.
Brzustowicz, Richard [24] demonstrated ChatGPT’s potential in automating library cataloging through
MARC record generation, while Adetayo [25] highlighted its broader applications in academic libraries,
including reference assistance and task automation, despite concerns about intellectual property, bias,
1
https://search.worldcat.org/
2
eScriptorium is an advanced digital platform designed to streamline the transcription, annotation, and analysis of both
printed and handwritten texts. Built on machine learning techniques, it provides a robust and flexible environment for
processing historical and modern documents in a wide range of languages and scripts.
3
https://lapira.diamondrda.org/
and job displacement. ChatGPT [26] exhibited notable proficiency in categorizing subjects within an
Islamic digital library; however, issues like interpretability, generalization, and hallucinations remain
key challenges.
Xu [27] explored AI applications in libraries, focusing on six technologies including OCR, NLP, and
machine learning. The study analyzed their roles, implementation challenges, and potential impact on
library development and reform. Kraken, an open-source OCR engine, achieves high-accuracy Arabic
OCR on al-Abhath. The evaluation compares typeface-specific and general models, identifies error
patterns, and recommends improving Arabic-script OCR through systematic training data, multilingual
modeling, and better segmentation techniques [28]. As AI continues to evolve, libraries must adapt to
emerging trends that will transform metadata creation, management, and utilization. Key developments
include linked data adoption, predictive analytics, enhanced interoperability, and shifting librarian roles
in metadata curation [29, 24, 1]. Moreover, several existing studies overlook the ethical and operational
challenges associated with integrating AI into metadata management [30, 31, 17]. Key issues such as
algorithmic bias, the risk of reduced human oversight, and the long-term sustainability of AI-driven
metadata systems are frequently neglected.
Several projects have focused on digitizing and developing Arabic and Persian text corpora, making
them highly relevant to our study within the digital domain. Notable initiatives include the Open
Islamiate Text Initiative (OpenITI) [32], Shamela4 , KITAB5 and the Persian Digital Library6 (PDL) , which
have significantly advanced research in this field.
3. Case Study of the La Pira Library
3.1. La Pira Library: A Center for Islamic Scholarship
Established in 1953, the Foundation for Religious Sciences (FSCIRE) began as a specialized research
center in the history of Christianity, housing the Dossetti Library in Bologna. Recognized in 2021 as a
European research infrastructure for religious sciences, FSCIRE expanded its focus. In 2018, it opened
the Giorgio La Pira Library in Palermo7 as an extension of the Dossetti Library. Modeled on FSCIRE’s
extensive expertise, this new library serves as a dedicated facility for studying the history, doctrines,
and theology of Islam. It boasts a vast collection of over 31,000 paper volumes and approximately
900,000 digital works, including significant manuscripts and texts in Western languages, Arabic, and
Persian, as well as a unique 18th-century Koranic manuscript
3.2. Integrated Overview of the Diamond Catalogue and La Pira Library’s
Classification Practices
The Diamond general catalogue8 , developed by the Dominican Institute for Oriental Studies (IDEO),
provides unified access to the collections of its partner institutions, including the La Pira Library.
Supporting cataloguing in multiple languages, including Arabic and others, it broadens its applicability
to diverse collections and user communities. Built upon the Resource Description and Access (RDA)
format, the catalogue incorporates the Library Reference Model (LRM) developed by the International
Federation of Library Associations and Institutions (IFLA). This model unifies previously separate frame-
works—FRBR, FRAD, and FRSAD—into a single, cohesive system, enhancing the clarity, consistency,
and interoperability of bibliographic records .
A central aspect of LRM-based cataloguing is the WEMI (Work, Expression, Manifestation, Item)
model, which classifies:
4
http://shamela.ws
5
https://kitab-project.org/
6
https://persdigumd.github.io
7
https://www.fscire.it/lang/eng/heritage/biblioteca-la-pira
8
hhttps://ideo.diamondrda.org
Figure 1: Monograph record from FSCIRE’s La Pira Library, extracted from the Diamond Catalogue and shown
in the Dublin Core metadata format.
Figure 2: Overview of the bibliographic metadata extracted from the Diamond Catalogue.
• Work: The fundamental intellectual or artistic creation, identified by a title. This level may involve
authors, compilers, or recipients.
• Expression: A specific realization of a Work, often distinguished by its language, edition, or format,
involving roles such as intellectual editors, translators, or illustrators.
• Manifestation: The physical or digital embodiment of an Expression, typically identified by
ISBN/ISSN, with responsibilities including publishers, printers, or copyright owners.
• Item: The individual physical or digital unit of a Manifestation, tracked by its call number and
possibly linked to donors, binders, or dedicatees.
While many libraries utilize subject headings and intricate thematic schemes to facilitate discovery,
the La Pira Library employs a distinct approach. Instead of traditional subject headings, it uses a
topographic classification system rooted in the physical layout of materials within the library. This
system is dynamic, allowing updates and expansions as the collection grows, and correlates each item’s
classification with its physical location on the shelves. This approach categorizes materials into broad
macro categories that are further subdivided into specific categories and micro-categories. Unlike
systems relying on multiple conceptual descriptors, each book’s classification at the La Pira Library
is directly tied to its placement within the library’s spatial configuration. This method, particularly
effective given the Library’s wide thematic scope—from religious texts to historical works,supports an
intuitive, location-based retrieval process that aligns with user navigation habits. The La Pira Library’s
application of a physical, topographically based system alongside the advanced cataloguing standards
of the Diamond catalogue exemplifies how libraries can adapt their cataloguing methods to best serve
their collections and communities. This dual approach, balancing modern standards with practical,
user-centered design, highlights the library’s commitment to both preserving scholarly resources and
enhancing accessibility.
4. Developing the Digital Maktaba prototype Using La Pira Library’s
Bibliographic Metadata
As part of the Digital Maktaba project’s early efforts, the Fscire research team [33] initiated the devel-
opment of a semi-automated tool aimed at cataloguing non-Latin texts, such as Arabic, Persian, and
Azerbaijani. This phase employed EasyOCR9 as OCR system to extract text and metadata from digitized
PDFs. While this technology aimed to enhance the efficiency of the Digital Maktaba’s cataloguing
processes, it faced several challenges. For instance, manual language specification was required before
processing, which slowed down operations. Additionally, the metadata from EasyOCR was often inac-
curate, with text boxes fragmented and incorrectly aligned due to the right-to-left structure of Arabic
script. These inaccuracies resulted in significant errors in metadata indexing, adversely impacting
retrieval accuracy. To enhance output quality, Google Docs was utilized to automatically infer the
language of the documents. However, this approach failed to generate the necessary metadata, thereby
limiting its utility for cataloguing. Additionally, the costs associated with API usage imposed further
restrictions on its effectiveness.
Building on these foundational experiences, we are now pivoting the Digital Maktaba project towards
a more robust integration of bibliographic metadata. In this next phase, we will leverage the extensive
and already validated metadata from the La Pira Library, which includes over 15,000 catalogued entries.
By employing metadata that has been rigorously validated by expert librarians, we aim to significantly
enhance the accuracy and utility of our digital cataloguing efforts. Furthermore, this integration will
involve collaboration with the La Pira Library’s IT team to ensure seamless data migration and system
compatibility.
This strategic shift not only addresses the limitations encountered in the initial OCR workflows
but also leverages the established cataloguing standards and practices of the La Pira Library. By
incorporating validated metadata, we ensure that digital representations of texts are precise and truly
reflective of their physical counterparts. This advancement aims to support more sophisticated retrieval
and research capabilities, such as advanced search filters and cross-referencing features.
In the following section, we outline the specific methodology employed in leveraging La Pira Library’s
extensive bibliographic metadata for the development of the Digital Maktaba prototype. This systematic
approach is crucial for transforming raw data into a structured, accessible digital library format that
serves both academic researchers and the general public. Our methodology underscores the pivotal role
of bibliographic metadata as the backbone of a functional digital library. Rather than treating metadata
as an afterthought, we place it at the center of the entire digitization, training, and integration pipeline.
4.1. Methodology
This section describes the various steps required for the development of Digital Maktaba prototype as
illustrated in Figure 3.
As this project is ongoing, not all functionalities have been fully implemented yet. Currently, our
efforts have been concentrated on the initial phase: metadata extraction. We have successfully extracted
comprehensive metadata from the La Pira Library’s collections, and are now focusing on the critical
task of labeling. Once the labeling work is completed, the data will undergo a thorough review by a
linguistic expert specializing in Arabic to ensure its accuracy and reliability.
Metadata Extraction. The first step involves generating an annotated dataset of Arabic documents
along with images of their frontispieces. We began with a dataset of 15,900 documents obtained from
the Diamond catalogue, as shown in Figure 1. Each document is associated with a Uniform Resource
Identifier (URI) (e.g., https://lapira.diamondrda.org/manifestation/290168) that links to its corresponding
9
https://github.com/JaidedAI/EasyOCR
Figure 3: Key Steps in the Workflow of the Digital Maktaba Prototype
page in the La Pira Library’s catalogue. Our primary focus is on Arabic-language documents that
include an image of the frontispiece, as these are essential for training our Kraken OCR model. To
identify and collect these resources, we developed a web crawler using the Scrapy library10 . This crawler
processes each document’s URI to extract metadata such as language, classification, and, when available,
downloads the frontispiece image. From the initial dataset, 5,900 documents were identified as being
written in Arabic, and 2,200 of these included an available frontispiece image.
For the Digital Maktaba project, we adapted La Pira’s topographic classification system11 by inte-
grating a subject headings section to the metadata extracted from the Diamond catalog (refer to Figure
2). The subjects are based on the topics provided by the library’s classification system (as shown in
Figure 1), which categorizes the materials into thematic areas such as Philosophy and Sciences, Classical
Islamic Philosophy, and Avicenna and Avicennism. This adaptation allowed us to structure the data
in a way that improved resource discovery and metadata organization. Furthermore, the inclusion of
subject headings has enriched the metadata, which will improve search and retrieval capabilities within
the Digital Maktaba prototype.
OCR Training with Kraken. In this phase, we aim to construct an annotated dataset that links each
document’s metadata (e.g., title, authors, publisher) with the corresponding text boxes extracted from
its frontispiece. This process will be carried out using the eScriptorium interface, which utilizes the
open-source Kraken OCR model. The Kraken model, provided to us by the eScriptorium development
team, has an initial accuracy of 96.4% for Arabic script recognition and will serve as the foundation
for further training on the frontispieces of Arabic books in our collection. The tool identifies and
extracts different text boxes from the frontispiece images, retrieves the text from each box, and allows
for manual correction to align with the metadata. An example of this process is illustrated in Figure 4.
The completed annotated dataset will then be used to further train the Kraken OCR model, refining its
performance specifically for this task and improving its accuracy on our dataset. In a subsequent phase,
this trained model will be integrated into the Digital Maktaba prototype, replacing the EasyOCR model
currently integrated into the existing prototype (see Figure 5). This integration is expected to enhance
the overall accuracy and efficiency of text retrieval within the digital library system.
System Architecture. We will develop a modular system that includes a scalable metadata repository
and an intuitive user interface for managing documents. This tool will enable authorized users to
catalogue new documents using the pre-trained Kraken OCR model, as depicted in Figure 5, facilitating
their addition to the library. Additionally, the system will offer an advanced querying interface, allowing
users to efficiently search through the already catalogued documents.
Data Integration. We will also provide the capability to encode the generated metadata in XML for-
mats following standards such as MARCXML and METS/ALTO. This approach ensures interoperability
and scalability across our tool and other platforms, facilitating seamless data exchange and integration
with existing library systems.
Future Alignment with WorldCat and Europeana: We plan to adopt the Europeana Data Model
(EDM) to enrich semantic relationships and enhance multilingual support, essential for representing
our diverse cultural heritage accurately. By integrating comprehensive bibliographic details such as
10
https://scrapy.org
11
https://lapira.diamondrda.org/all/classifications
Figure 4: The eScriptorium interface used for training the open-source Kraken OCR model.
Figure 5: Screenshot of the Title and Author section of the Digital Maktaba prototype interface.
author information, publication data, and subjects, mirroring the structures used by WorldCat and the
La Pira Library, we aim to improve interoperability with global library systems. This approach will
facilitate smoother data exchange and integration, boosting the discoverability and accessibility of our
collections.
5. Conclusion and Future Work
The intricate nature of Arabic script, characterized by ligatures, diacritics, and its right-to-left (RtL)
orientation, poses significant challenges for OCR systems. Ongoing fine-tuning of Kraken models is a
key component of efforts to enhance recognition accuracy. Preliminary endeavors, encompassing data
preparation and initial testing, have demonstrated promising potential for these models to effectively
process Arabic texts. Furthermore, this project addresses the variability in metadata formats by adopting
universal standards such as Dublin Core and XML-based encoding. Plans to incorporate iterative user
testing aim to refine the platform, ensuring it meets the diverse needs of its user base.
Initial results validate the feasibility of extracting and integrating bibliographic metadata from the
Diamond catalogue, resulting in a prototype system that enables efficient metadata management and
user access. These outcomes highlight the transformative impact of metadata-driven approaches in the
design of digital libraries.
This study establishes a comprehensive framework for developing an Arabic digital library by tackling
critical challenges and utilizing bibliographic metadata as a foundational element. Future work will focus
on expanding the metadata repository, integrating advanced machine learning techniques to further
enhance text recognition, and implementing user engagement strategies to improve the platform’s
usability and accessibility. This includes finalizing a comprehensive dataset of Arabic frontispiece
images and collaborating with linguistic experts to ensure linguistic accuracy. By doing so, this effort
will not only preserve and increase access to Arabic texts in digital libraries, supporting advanced
cataloguing and research initiatives, but also pave the way for robust, user-friendly digital library
solutions tailored to the unique complexities of Arabic script.
Acknowledgments
This work was conducted within the PNRR project ITSERR - Italian Strengthening of the ESFRI RI
RESILIENCE" (Avviso MUR 3264/2022) funded by EU – NextGenerationEU - Grant No IR0000014.
References
[1] A. Tella, O. Akanmu Odunola, L. WO, Cataloguing and classification in the era of artificial
intelligence: Benefits, and challenges from the perspective of cataloguing librarians in oyo state,
nigeria, Vjesnik bibliotekara Hrvatske 66 (2023) 159–176.
[2] D. Oyighan, E. S. Ukubeyinje, B. T. David-West, B. D. Oladokun, The role of ai in transforming
metadata management: Insights on challenges, opportunities, and emerging trends, Asian Journal
of Information Science and Technology 14 (2024) 20–26.
[3] M. Alghamdi, W. Teahan, Experimental evaluation of arabic ocr systems, PSU Research Review 1
(2017) 229–241.
[4] J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, J. Zhou, Qwen-vl: A versatile
vision-language model for understanding, localization, text reading, and beyond, 2023. URL:
https://api.semanticscholar.org/CorpusID:261101015.
[5] H. Liu, C. Li, Y. Li, Y. J. Lee, Improved baselines with visual instruction tuning, in: Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 26296–26306.
[6] N. I. A. El-Labban, The bibliographic ontologies and the bibliographic framework data model:
comparative study, Cybrarians Journal (2016).
[7] M. Z. Raza, N. F. Warraich, Impact of semantic web technologies on digital collections of libraries,
in: The 8th ALIEP Conference on the International Forum on Data, Information, and Knowledge
for Digital Lives, 2017.
[8] B. Haslhofer, A. Isaac, R. Simon, Knowledge graphs in the libraries and digital humanities domain,
arXiv preprint arXiv:1803.03198 (2018).
[9] Z. Raza, K. Mahmood, N. F. Warraich, Application of linked data technologies in digital libraries:
a review of literature, Library Hi Tech News 36 (2019) 9–12.
[10] Y. Hidalgo-Delgado, R. Estrada-Nelson, B. Xu, B. Villazon-Terrazas, A. Leiva-Mederos, A. Tello,
Methodological guidelines for publishing library data as linked data, in: 2017 International
Conference on Information Systems and Computer Science (INCISCOS), IEEE, 2017, pp. 241–246.
[11] F. Nafis, A. Yahyaouy, B. Aghoutane, Ontologies for the classification of cultural heritage data,
in: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems
(WITS), IEEE, 2019, pp. 1–7.
[12] I. C. Dorobăt, , V. Posea, Raising the interoperability of cultural datasets: the romanian cultural
heritage case study, in: Information Systems: 17th European, Mediterranean, and Middle Eastern
Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020, Proceedings 17,
Springer, 2020, pp. 35–48.
[13] H. S. Patrício, M. I. Cordeiro, P. N. Ramos, From the web of bibliographic data to the web of
bibliographic meaning: structuring, interlinking and validating ontologies on the semantic web,
International Journal of Metadata, Semantics and Ontologies 14 (2020) 124–134.
[14] T. Repke, R. Krestel, Interactive curation of semantic representations in digital libraries, in:
Towards Open and Trustworthy Digital Societies: 23rd International Conference on Asia-Pacific
Digital Libraries, ICADL 2021, Virtual Event, December 1–3, 2021, Proceedings 23, Springer, 2021,
pp. 219–229.
[15] S. E. Alamri, Ontology Extraction from an Arabic Book, Kent State University, 2020.
[16] D. D. W. Praveenraj, K. Agarwal, B. Kim, V. Singh, Artificial intelligence applications in modern
library services, Library Progress International 45 (2025) 1–11.
[17] S. Priya, R. Ramya, Future trends and emerging technologies in ai and libraries, Applications of
Artificial Intelligence in Libraries (2024) 245–271.
[18] M. Bairagi, S. R. Lihitkar, Optimizing library services through the integration of artificial intelli-
gence tools and techniques, in: Applications of Artificial Intelligence in Libraries, IGI Global, 2024,
pp. 193–222.
[19] M. Q. Affum, O. K. Dwomoh, Investigating the potential impact of artificial intelligence in
librarianship, Library Philosophy and Practice (2023) 1–12.
[20] M. Q. Affum, The role of artificial intelligence in library automation., Library Philosophy &
Practice (2023).
[21] L. Abudulsalami, A. K. Queeneth, S. S. NKapia, H. N. Ligola, E. L. Ovigue, B. O. Obande, M. Bilal,
Artificial intelligence in academic libraries and its impact on library services and operations,
Omanarp International Journal of Library & Information Science 1 (2024) 53–61.
[22] H. A. Farag, S. N. Mahfouz, S. Alhajri, Artificial intelligence investing in academic libraries: Reality
and challenges, Library Philosophy and Practice (2021) 1–34.
[23] A. Hussain, Use of artificial intelligence in the library services: prospects and challenges, Library
Hi Tech News 40 (2023) 15–17.
[24] R. Brzustowicz, From chatgpt to catgpt: the implications of artificial intelligence on library
cataloging, Information Technology and Libraries 42 (2023).
[25] A. J. Adetayo, Artificial intelligence chatbots in academic libraries: the rise of chatgpt, Library Hi
Tech News 40 (2023) 18–21.
[26] A. El Ganadi, R. A. Vigliermo, L. Sala, M. Vanzini, F. Ruozzi, S. Bergamaschi, et al., Bridging islamic
knowledge and ai: Inquiring chatgpt on possible categorizations for an islamic digital library (full
paper), in: CEUR Workshop Proceedings, volume 3536, 2023, pp. 21–33.
[27] Z. Xu, Research on the application of artificial intelligence in the library sector, in: Third
International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022),
volume 12610, SPIE, 2023, pp. 1420–1429.
[28] B. Kiessling, G. Kurin, M. T. Miller, K. Smail, Advances and limitations in open source arabic-script
ocr: A case study, arXiv preprint arXiv:2402.10943 (2024).
[29] J. Yoon, J. E. Andrews, H. L. Ward, Perceptions on adopting artificial intelligence and related
technologies in libraries: public and academic librarians in north america, Library Hi Tech 40
(2022) 1893–1915.
[30] P. Chhetri, Analyzing the strengths, weaknesses, opportunities, and threats of ai in libraries.,
Library Philosophy & Practice (2023).
[31] C. Mallikarjuna, An analysis of integrating artificial intelligence in academic libraries., DESIDOC
Journal of Library & Information Technology 44 (2024).
[32] M. T. Miller, M. G. Romanov, S. B. Savant, Digitizing the textual heritage of the premodern
islamicate world: Principles and plans, International Journal of Middle East Studies 50 (2018)
103–109. doi:10.1017/S0020743817000964.
[33] R. Martoglia, L. Sala, M. Vanzini, R. Vigliermo, et al., A tool for semiautomatic cataloguing of
an islamic digital library: a use case from the digital maktaba project (short paper), in: CEUR
WORKSHOP PROCEEDINGS, volume 3234, CEUR-WS, 2022.