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Automatic processing of Historical Arabic Documents: a comprehensive survey

Mohamed Ibn Khedher 1 Houda Jmila 2, 3, 4 Mounim El Yacoubi 5, 3, 6
4 R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
6 ARMEDIA-SAMOVAR - ARMEDIA
SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
Abstract : Nowadays, there is a huge amount of Historical Arabic Documents (HAD) in the national libraries and archives around the world. Analyzing this type of data manually is a difficult and costly task. Thus, an automatic process is required to exploit these documents more rapidly. Processing historical documents is a recent research subject that has seen a remarkable growth in the last years. Processing Historical Arabic Documents is a particularly challenging problem. First, due to complicated nature of Arabic script compared to other scripts and second because the documents are ancient. This paper focuses on this difficult problem and provides a comprehensive survey of existing research work. First, we describe in detail the challenges making the automatic processing of Historical Arabic Documents a difficult task. Second, we classify this task into four applications of automatic processing of HAD: i) Analyze the document to extract the main text ii) Identify the writer of the document iii) Recognize some words or parts of the document in a reference dataset andiv) Retrieve and extract specific data from the document. For each application, existing approaches are surveyed and qualitatively described. Finally, we focus on available datasets and describe how they can be used in each application.
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https://hal.archives-ouvertes.fr/hal-02481354
Contributor : Mounîm A. El Yacoubi <>
Submitted on : Monday, February 17, 2020 - 2:02:39 PM
Last modification on : Wednesday, February 19, 2020 - 1:03:59 AM

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Mohamed Ibn Khedher, Houda Jmila, Mounim El Yacoubi. Automatic processing of Historical Arabic Documents: a comprehensive survey. Pattern Recognition, Elsevier, 2020, 100, pp.107144-1:107144-17. ⟨10.1016/j.patcog.2019.107144⟩. ⟨hal-02481354⟩

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