Deep Learning Networks for Off-Line Handwritten Signature Recognition
Reliable identification and verification of off-line handwritten signatures from images is a difficult problem with many practical applications. This task is a difficult vision problem within the field of biometrics because a signature may change depending on psychological factors of the individual. Motivated by advances in brain science which describe how objects are represented in the visual cortex, advanced research on deep neural networks has been shown to work reliably on large image data sets. In this paper, we present a deep learning model for off-line handwritten signature recognition which is able to extract high-level representations. We also propose a two-step hybrid model for signature identification and verification improving the misclassification rate in the well-known GPDS database.
KeywordsDeep Learning Generative Models Signature Recognition
Unable to display preview. Download preview PDF.
- 1.Ackley, D., Hinton, G., Sejnowski, T.: A learning algorithm for Boltzmann Machines. Science 9(1), 147–169 (1985)Google Scholar
- 5.Bluemenstein, M., Liu, X.Y.: A modified direction feature for cursive character recognition. In: IEEE-IJCNN, pp. 2983–2987 (2004)Google Scholar
- 8.Gonçalves, I., Santos, S.: Off-line signatures verification system. Tech. Rep. trp-#10/11, University of Coimbra, Portugal (2011)Google Scholar
- 12.Longcamp, M., Boucard, C., Gilhodes, J.C., Anton, J.L., Roth, M., Nazarian, B., Velav, J.L.: Learning through hand- or typewriting influences visual recognition of new graphic shapes: Behavioral and functional imaging evidence. Science Journal of Cognitive Neuroscience 20, 802–815 (2008)CrossRefGoogle Scholar
- 14.Nguyen, V., Blumenstein, M., Vallipuram, M., Leedham, G.: Off-line signature verification using enhanced modified direction features in conjunction with neural classifiers and support vector machines. In: IEEE-ICDAR, pp. 1300–1304 (2009)Google Scholar
- 15.Yu, K., Xu, W., Gong, Y.: Deep learning with kernel regularization for visual recognition. In: Neural Information Processing Systems, NIPS (2009)Google Scholar