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Title: A Deep Learning based System for Writer Identification in Handwritten Arabic Historical Manuscripts
Authors: Chammas, Michel 
Affiliations: Institute of History Archeology and Near Eastern Studies 
Co-authors: Makhoul, Abdallah
Demerjian, Jacques
Dannaoui, Elie 
Keywords: Writer identification
Document Analysis
Deep learning
Machine learning
historical documents
Subjects: Writer Identification
Issue Date: 2022
Publisher: Springer
Part of: Multimedia Tools and Applications
Determining the writer or transcriber of historical Arabic manuscripts has always been a major challenge for researchers in the field of humanities. With the development of advanced techniques in pattern recognition and machine learning, these technologies have been applied to automate the extraction of paleographical features in order to solve this issue. This paper presents a baseline system for writer identification, tested on a Historical Arabic dataset of 11610 single and double folio images. These texts were extracted from a unique collection of 567 Historical Arabic Manuscripts available at the Balamand Digital Humanities Center. A survey has been conducted on the available Arabic datasets and previously proposed techniques and algorithms. The Balamand dataset presents an important challenge due to the geo-historical identity of manuscripts and their physical conditions. An advanced Deep Learning system was developed and tested on three different Latin and Arabic datasets: ICDAR19, ICFHR20 and KHATT, before testing it on the Balamand dataset. The system was compared with many other systems and it has yielded a state-of-the-art performance on the new challenging images with 95.2% mean Average Precision (mAP) and 98.1% accuracy.
Type: Journal Article
Appears in Collections:Institute of History Archeology and Near Eastern Studies

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