استخدام تقنية الذكاء الاصطناعى فى رقمنة الوثائق والمخطوطات: نظام Transkribus – نموذجا-

The importance of this research lies in the interest in the field of documents and manuscripts and benefiting from the investment of modern technological techniques in digitizing documents and manuscripts. The Arab manuscript heritage constitutes an important part of the history of Arab and Islamic civilization, and represents a valuable source of knowledge and culture. With the development of technology and the emergence of artificial intelligence applications, this heritage has a new opportunity to benefit from and preserve it. Certainly, artificial intelligence applications enable us to convert Arabic manuscripts into digital texts that can be easily accessed, which helps preserve this valuable heritage. The Transkribus platform provides services and tools for digitizing, copying, identifying and searching historical documents. It is the only platform worldwide that has enabled non-technical users to train their machine learning-based neural networks and apply them to their documents in order to create automated copies and make them searchable by discovering keywords. Transkribus is used by thousands of users and hundreds of archives, libraries and research groups around the world. In this research, we briefly describe the approach followed by the platform in terms of the basic business and governance model, in addition to the technical aspects of the platform.

Authors

Publication Details

Journal
المجلة الدولية لعلوم المكتبات و المعلومات
Published
2026-01-01
DOI
https://doi.org/10.21608/ijlis.2025.399851.1299
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

استخدام تقنية الذكاء الاصطناعى فى رقمنة الوثائق والمخطوطات: نظام Transkribus – نموذجا-

Sahar محمد
المجلة الدولية لعلوم المكتبات و المعلومات
Handwritten Text Recognition Techniques
article

استخدام تقنية الذكاء الاصطناعى فى رقمنة الوثائق والمخطوطات: نظام Transkribus – نموذجا-

Sahar محمد
article en

Abstract

The importance of this research lies in the interest in the field of documents and manuscripts and benefiting from the investment of modern technological techniques in digitizing documents and manuscripts. The Arab manuscript heritage constitutes an important part of the history of Arab and Islamic civilization, and represents a valuable source of knowledge and culture. With the development of technology and the emergence of artificial intelligence applications, this heritage has a new opportunity to benefit from and preserve it. Certainly, artificial intelligence applications enable us to convert Arabic manuscripts into digital texts that can be easily accessed, which helps preserve this valuable heritage. The Transkribus platform provides services and tools for digitizing, copying, identifying and searching historical documents. It is the only platform worldwide that has enabled non-technical users to train their machine learning-based neural networks and apply them to their documents in order to create automated copies and make them searchable by discovering keywords. Transkribus is used by thousands of users and hundreds of archives, libraries and research groups around the world. In this research, we briefly describe the approach followed by the platform in terms of the basic business and governance model, in addition to the technical aspects of the platform.

المجلة الدولية لعلوم المكتبات و المعلوماتVol. 13(1.1)
Sustainable cities and communities
Openalex Percentile: Top 97%
Handwritten Text Recognition Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.