Deep Learning Technique for Improving the Recognition of Handwritten Signature

Authors

  • Anusara Hirunyawanakul, Supaporn Bunrit, Nittaya Kerdprasop, and Kittisak Kerdprasop Author

Keywords:

Deep learning, deep convolutional neural networks, handwritten signature recognition, transfer learning.

Abstract

Handwritten signature recognition is a biometric task used extensively in our daily life. The efficacy of such system is important and challenging in that the recognition accuracy still has room for improvement. In this paper, we propose the use of Deep Convolutional Neural Networks (DCNN), which is a deep learning technique, to improve accuracy of handwritten signature recognition. We apply 
DCNN in two difference strategies for signature recognition: 1) transfer learning using leveraged features from a pre-trained 
model on a larger dataset, and 2) create CNN model from scratch. Our studied dataset consists of 600 pictures of handwritten signatures collected from 30 people. 

Downloads

Download data is not yet available.

Downloads

Published

12.11.2024

How to Cite

Deep Learning Technique for Improving the Recognition of Handwritten Signature . (2024). International Journal of Information and Electronics Engineering, 11(2), 1-7. http://ijiee.org/index.php/ijiee/article/view/773