Multilingual online access to digitised Arabic manuscripts by using metadata

Soualah, Mohammed Ourabah; Yahia, Yassine Ait Ali; Hassoun, Mohamed; Guessoum, Abderrezak

doi:10.1504/ijmso.2012.051485

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…But, huge efforts are required to extract information from these manuscripts, index them, and make them easily accessible and retrieved [5]. For unknown manuscripts, it is essential to identify meta-information and extract catalog-related information, such as (1) author, (2) language, (3) subject, (4) topics, and (5) publishing date [6], which is used for indexing purposes in libraries. However, given the huge number and degraded quality of such manuscripts, manual extraction of information from the manuscript is challenging [7].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Arabic Manuscripts Classification

Al-homed,

Jambi,

Al-Barhamtoshy

2023

Sensors

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For centuries, libraries worldwide have preserved ancient manuscripts due to their immense historical and cultural value. However, over time, both natural and human-made factors have led to the degradation of many ancient Arabic manuscripts, causing the loss of significant information, such as authorship, titles, or subjects, rendering them as unknown manuscripts. Although catalog cards attached to these manuscripts might contain some of the missing details, these cards have degraded significantly in quality over the decades within libraries. This paper presents a framework for identifying these unknown ancient Arabic manuscripts by processing the catalog cards associated with them. Given the challenges posed by the degradation of these cards, simple optical character recognition (OCR) is often insufficient. The proposed framework uses deep learning architecture to identify unknown manuscripts within a collection of ancient Arabic documents. This involves locating, extracting, and classifying the text from these catalog cards, along with implementing processes for region-of-interest identification, rotation correction, feature extraction, and classification. The results demonstrate the effectiveness of the proposed method, achieving an accuracy rate of 92.5%, compared to 83.5% with classical image classification and 81.5% with OCR alone.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Arabic Manuscripts Classification

Al-homed,

Jambi,

Al-Barhamtoshy

2023

Sensors

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Dynamic cataloguing of the old Arabic manuscripts by automatic extraction of metadata

Soualah

Yahia

Keita

et al. 2017

LHT

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Purpose The purpose of this paper is to obtain online access to the digitised Arabic manuscripts images, which need to use a catalogue. The bibliographic cataloguing is unsuitable for old Arabic manuscripts, and it is imperative to establish a new cataloguing model. In the research, the authors propose a new cataloguing model based on manuscript annotations and transcriptions. This model can be an effective solution to dynamic catalogue old Arabic manuscripts. In this field, the authors used the automatic extraction of the metadata that is based on the structural similarity of the documents. Design/methodology/approach This work is based on experimental methodology. The whole proposed concepts and formulas were tested for validation. This, allows the authors to make concise conclusions. Findings Cataloguing old Arabic manuscripts faces problem of unavailability of information. However, this information may be found in another place in a copy of the original manuscript. Thus, cataloguing Arabic manuscript cannot be done in one time, it is a continual process which require information updating. The idea is to make a pre-cataloguing of a manuscript, then try to complete and improve it through a specific platform. Consequently, in the research work, the authors propose a new cataloguing model, which the authors call “Dynamic cataloguing”. Research limitations/implications The success of the proposed model is confronted with the involvement of all actors of the model. It is based on the conviction and the motivation of actors of the collaborative platform. Practical implications The model can be used in several cataloguing fields, where the encoding model is based on XML. The model is innovative and implements a smart cataloguing model. The model is useful by using a web platform. It allows an automatic update of a catalogue. Social implications The model prompts the user to participate and enrich the catalogue. The user could improve his social status from a passive to an active. Originality/value The dynamic cataloguing model is a new concept. It has never been proposed in the literature until now. The proposed cataloguing model is based on automatic extraction of metadata from user annotations/transcription. It is a smart system which automatically updates or fills the catalogue with the extracted metadata.

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Multilingual online access to digitised Arabic manuscripts by using metadata

Cited by 2 publications

References 14 publications

A Deep Learning Approach for Arabic Manuscripts Classification

A Deep Learning Approach for Arabic Manuscripts Classification

Dynamic cataloguing of the old Arabic manuscripts by automatic extraction of metadata

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