Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution towards the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification involving non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English signature verification. To fill this gap, a threshold-based scheme for verification considering off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/endpoint and directional chain code are employed for feature extraction. The Nearest Neighbour method is considered for classification. Furthermore, a Bangla signature database, which consists of 2400 (100×24) genuine signatures and 3000 (100×30) forgeries has been created and is employed for experimentation. We obtained a 15.57% Average Error Rate (AER) as the best verification result using directional chain code features employed in this research work.
Conference proceeding
Off-line Bangla signature verification: an empirical study
Proceedings of the International Joint Conference on Neural Networks (IJCNN)
Proceedings of the International Joint Conference on Neural Networks (IJCNN) (Dallas, USA, 4-9 August)
2013
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Abstract
Details
- Title
- Off-line Bangla signature verification: an empirical study
- Creators
- Srikanta Pal - Griffith UniversityAli Reza Alaei - Université François-Rabelais, FranceUmapada PalMicahael Blumenstein - Griffith University
- Publication Details
- Proceedings of the International Joint Conference on Neural Networks (IJCNN)
- Conference
- Proceedings of the International Joint Conference on Neural Networks (IJCNN) (Dallas, USA, 4-9 August)
- Publisher
- IEEE; USA
- Identifiers
- 2008; 991012821991202368
- Academic Unit
- Information Technology; Faculty of Science and Engineering; School of Business and Tourism; Faculty of Business, Law and Arts
- Resource Type
- Conference proceeding