Abstract
In order to improve the offline handwritten signature recognition effect, an offline handwritten signature recognition method based on discrete curvelet transform is proposed. First, the necessary pre-processing of offline handwritten signatures is carried out, including grayscale, binarization, smooth denoising, etc. The pre-processed signature image is subjected to curvelet transform to obtain real-numbered curve coefficients in the cell matrix, and a total of 82-dimensional energy features are extracted, and multi-scale block local binary mode (MBLBP) is combined on the cell matrix of discrete curvelet transform to form a new signature feature, use the SVM classifier for training and classification. Experiments on two databases, Uyghur and Kirgiz, the highest accuracy was 97.95% and 97.42% respectively. The experimental results show that the proposed method has better accuracy in offline handwritten signature recognition.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tanwar, S., Obaidat, M.S., Tyagi, S., Kumar, N.: Online signature-based biometric recognition. In: Obaidat, M.S., Traore, I., Woungang, I. (eds.) Biometric-Based Physical and Cybersecurity Systems, pp. 255–285. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98734-7_10. Chapter 10
Das, S.D., Ladia, H., Kumar, V., et al.: Writer independent offline signature recognition using ensemble learning (2019)
Puhan, N.B., Manoj Kumar, M.: Off-line signature verification: upper and lower envelope shape analysis using chord moments. IET Biometrics 3(4), 347–354 (2014)
Narkhede, P., Ingle, V.R.: Offline handwritten signature recognition using artificial neural network techniques. In: International Conference on Quality Up-gradation in Engineering, Science and Technology, pp. 21–24 (2015)
Joshi, S., Kumar, A.: Feature extraction using DWT with application to offline signature identification. In: Mohan, S., Suresh Kumar, S. (eds.) ICSIP 2012. LNEE, vol. 222, pp. 285–294. Springer, India (2013). https://doi.org/10.1007/978-81-322-1000-9_27
Chen, J.: Research on offline recognition of handwritten signature based on Lagrangian support vector machine. Guangxi University (2012). (in Chinese)
Pham, T.-A., Le, H.-H. Toan, D.: Offline handwritten signature verification using local and global features. Ann. Math. Artif. Intell. 75 (2014). https://doi.org/10.1007/s10472-014-9427-5
Abri, G.L.: Uyghur offline signature recognition technology research. Xinjiang University (2012). (in Chinese)
Ubul, K., Yadkar, N., Aysa, A., Ibrahim, T.: Uyghur based on density characteristics offline Signature recognitio. Comput. Eng. Des. 37(08), 2200–2205 (2016). (in Chinese)
Yimin, A.H., Muti, M.L.M., Aisha, A., Ibira, T., Kurban, W.: High-dimensional statistical feature fusion of Uyghur offline Handwritten Signature Recognition. Comput. Sci. Explor. 12(02), 308–317 (2018). (in Chinese)
Sun, J., Song, J., Wu, X., et al.: Image segmentation method of lettuce leaf based on improved Otsu algorithm. J. Jiangsu Univ. 2, 179–184 (2018)
Sahare, P., Chaudhari, R.E., Dhok, S.B.: Word level multi-script identification using curvelet transform in log-polar domain. IETE J. Res. 2, 1–23 (2018)
Nath, V.K., Hatibaruah, R., Hazarika, D.: An efficient multiscale wavelet local binary pattern for biomedical image retrieval (2018)
Li, J., Weng, Z., Xu, H., et al.: Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: a cross-validated study. Eur. J. Radiol. 98, 61–67 (2018)
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant (No. 61862061, 61563052, 61163028), and 2018 years Scientific Research Initiate Program of Doctors of Xinjiang University under Grant No. 24470.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mo, LF., Mahpirat, Zhu, YL., Mamat, H., Ubul, K. (2019). Off-Line Handwritten Signature Recognition Based on Discrete Curvelet Transform. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-31456-9_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31455-2
Online ISBN: 978-3-030-31456-9
eBook Packages: Computer ScienceComputer Science (R0)