Development of offline handwritten signature authentication using artificial neural network
Handwritten signatures are playing an important role in finance, banking and education and more because it is considered to be the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using Art...
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IEEE
2018
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| Online Access: | http://irep.iium.edu.my/61256/ http://irep.iium.edu.my/61256/ http://irep.iium.edu.my/61256/ http://irep.iium.edu.my/61256/13/61256%20%20Development%20of%20Offline%20Handwritten.pdf |
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iium-612562018-09-16T17:28:19Z http://irep.iium.edu.my/61256/ Development of offline handwritten signature authentication using artificial neural network Gunawan, Teddy Surya Mahamud, Norsalha Kartiwi, Mira TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Handwritten signatures are playing an important role in finance, banking and education and more because it is considered to be the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using Artificial Neural Network (ANN). As part of the feature extraction, two image filters were used, i.e. Canny edge detector and averaging filter. A feedforward neural network with 1 hidden layer was trained using backpropagation algorithm. The number of nodes in the hidden layer was varied from 80 to 1000. The higher the number of nodes, the higher the recognition rate. Moreover, we found that Canny edge detector is the suitable feature extraction as it produced higher recognition rate compared to the averaging filter. IEEE 2018-03-08 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/61256/13/61256%20%20Development%20of%20Offline%20Handwritten.pdf Gunawan, Teddy Surya and Mahamud, Norsalha and Kartiwi, Mira (2018) Development of offline handwritten signature authentication using artificial neural network. In: International Conference on Computing, Engineering, and Design (ICCED 2017), 23-25 November 2017, Kuala Lumpur, Malaysia. http://ieeexplore.ieee.org/document/8308128/ 10.1109/CED.2017.8308128 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Gunawan, Teddy Surya Mahamud, Norsalha Kartiwi, Mira Development of offline handwritten signature authentication using artificial neural network |
| description |
Handwritten signatures are playing an important role in finance, banking and education and more because it is considered to be the “seal of approval” and remains the most preferred means of authentication. In this paper, an offline handwritten signature authentication algorithm is proposed using Artificial Neural Network (ANN). As part of the feature extraction, two image filters were used, i.e. Canny edge detector and averaging filter. A feedforward neural network with 1 hidden layer was trained using backpropagation algorithm. The number of nodes in the hidden layer was varied from 80 to 1000. The higher the number of nodes, the higher the recognition rate. Moreover, we found that Canny edge detector is the suitable feature extraction as it produced higher recognition rate compared to the averaging filter. |
| format |
Conference or Workshop Item |
| author |
Gunawan, Teddy Surya Mahamud, Norsalha Kartiwi, Mira |
| author_facet |
Gunawan, Teddy Surya Mahamud, Norsalha Kartiwi, Mira |
| author_sort |
Gunawan, Teddy Surya |
| title |
Development of offline handwritten signature authentication using artificial neural network |
| title_short |
Development of offline handwritten signature authentication using artificial neural network |
| title_full |
Development of offline handwritten signature authentication using artificial neural network |
| title_fullStr |
Development of offline handwritten signature authentication using artificial neural network |
| title_full_unstemmed |
Development of offline handwritten signature authentication using artificial neural network |
| title_sort |
development of offline handwritten signature authentication using artificial neural network |
| publisher |
IEEE |
| publishDate |
2018 |
| url |
http://irep.iium.edu.my/61256/ http://irep.iium.edu.my/61256/ http://irep.iium.edu.my/61256/ http://irep.iium.edu.my/61256/13/61256%20%20Development%20of%20Offline%20Handwritten.pdf |
| first_indexed |
2023-09-18T21:26:52Z |
| last_indexed |
2023-09-18T21:26:52Z |
| _version_ |
1777412250361921536 |