Deep Learning Technique for Improving the Recognition of Handwritten Signature
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
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.