Writer Identification from Nordic Historical Manuscripts using Transformer Networks
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Joint Conference on Biometrics (IJCB), 2024, 00
- Issue Date:
- 2024-03-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1715808.pdf | Published version | 5.93 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Handwriting has been used as a form of authentication for the last 1000 years Forensic analysis of handwriting using computers has been in practice since the 1970s With the evolution of deep learning techniques over the last decade such automated forensic analysis of handwritten text has become dependent on deep learning techniques In this paper we investigate the prowess of transformer networks in the context of identifying the writer of a handwritten sample We here propose a deep feature embedding based transformer network WiT for writer identification Experiments were conducted on a historical Nordic manuscript dataset comprising 9253 handwritten samples scribbled by 50 writers for the very first time and encouraging results were obtained Rigorous experiments were also conducted to check the noise damage resiliency of WiT and the outcomes were quite promising
Please use this identifier to cite or link to this item: