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. In order to evaluate the effectiveness of the proposed method, the accuracy is compared with the results obtained from various machine learning methods. The comparison reveals very satisfied recognition results in the sense that the two proposed strategies achieve 100% of the recognition rate. To compare the two
strategies in terms of training time, the strategy of creating DCNN model from scratch shows much lower training time than the transfer learning strategy.
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