Authors: Ćosović, Marijana
Janković, Radmila 
Amelio, Alessia
Affiliations: Computer Science 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: A Survey on Writer Identification and Recognition Methods with a Special Focus on Cultural Heritage
Volume: 3246
First page: 44
Last page: 54
Related Publication(s): Proceedings
Conference: 1st International Workshop on Digital Platforms and Resources for Access to Literary Heritage
Issue Date: 2022
Rank: M33
This paper reviews the state-of-the-art contributions for writer identification and recognition with a special focus on applications in the domain of cultural heritage. The task of writer recognition has only recently been recognized as a problem that can be solved by the methods available in the computer vision domain. A number of researchers have explored the performance of deep learning and transfer learning techniques for writer identification in historical documents, and for this purpose various datasets have been used, including the Avila Bible dataset, Historical-WI, HisFragIR20, IAM, HWDB and others. This paper analyses relevant methods used for writer identification and recognition in historical and medieval documents. It also makes a distinction between classification based on words, patches, or whole pages. The results indicate that the current literature supports using deep learning and transfer learning methods, as they are found to achieve the highest performance.
Keywords: Cultural heritage | Image Recognition | Survey | Writer Recognition

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