Please use this identifier to cite or link to this item:
Title: An End-to-End deep learning system for writer identification in handwritten Arabic manuscripts
Authors: Chammas, Michel 
Affiliations: Department of Computer Science 
Co-authors: Makhoul, Abdallah
Demerjian, Jacques
Dannaoui, Elie 
Keywords: Writer identification
historical documents
Deep learning
Document Analysis
Arabic manuscripts
Issue Date: 2023-12-06
Publisher: Springer
Part of: Multimedia Tools and Applications
The extraction of paleographical features is an important task to study the identity of the text in the Historical Manuscripts. One of the major features is the identification of the writer or copyist. Many researchers have worked on an automated system for writer identification, and with the development of deep learning techniques many approaches have been proposed. Most of the previous studies have developed a multi-steps system, while very few of them performed an End-to-End approach. Most of the systems rely on a pre-processing step to prepare the data in order to facilitate recognition. This paper presents an End-to-End deep learning system for writer identification, tested on four different datasets: ICDAR19 and ICFHR20 (Latin datasets), KHATT and Balamand (Arabic datasets). The system is based on the Deep-TEN approach using a customized ResNet-50 network for features and local descriptor extraction with an integration of a NetVLAD end-layer to compute and encode the global descriptor. It was compared with our state-of-the-art system, winner of ICFHR20 HisFrag competition, and showed an interesting performance on all datasets without any pre-processing techniques.
Type: Journal Article
Appears in Collections:Institute of History Archeology and Near Eastern Studies

Show full item record

Record view(s)

checked on Apr 23, 2024

Google ScholarTM


Dimensions Altmetric

Dimensions Altmetric

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.