1
Master of Science in Electrical Engineering, University of Birjandad
2
Associate Professor, Faculty of Electrical Engineering, University of Birjand
Abstract
In this study, a new method is presented for offline signature recognition. To compare the features, a new similarity measure is introduced based on the number of matched features. In addition, in the post-processing step, an innovative, efficient and effective coordinate matching filter is used that has low computational cost and is consistent with the feature extraction algorithm. This filter applies a threshold on Cartesian coordinate difference between the two blocks on the corresponding images. The implementation of the proposed system includes optimized features that are invariant to scaling and rotation changes. Using the new similarity criteria for matching these features, and post-processing routine using the proposed coordinates filter, applied on the GPDS960 (Offline) and SVC2004 (online converted to online) database, improved efficiency of the proposed identification system. Also proposed system parameters are selected and personalized automatically only once by using a genetic algorithm for each database.
khoshbaten, M., Razavi, S. M., & Mehrshad, N. (2015). Designing Coordinate Matching Filter along with the Extraction of Local Features to Improve the Accuracy of Handwriting Signature Recognition. Journal of Machine Vision and Image Processing, 2(1), 33-43.
MLA
Mahdi khoshbaten; Seyed Mohammad Razavi; Naser Mehrshad. "Designing Coordinate Matching Filter along with the Extraction of Local Features to Improve the Accuracy of Handwriting Signature Recognition". Journal of Machine Vision and Image Processing, 2, 1, 2015, 33-43.
HARVARD
khoshbaten, M., Razavi, S. M., Mehrshad, N. (2015). 'Designing Coordinate Matching Filter along with the Extraction of Local Features to Improve the Accuracy of Handwriting Signature Recognition', Journal of Machine Vision and Image Processing, 2(1), pp. 33-43.
VANCOUVER
khoshbaten, M., Razavi, S. M., Mehrshad, N. Designing Coordinate Matching Filter along with the Extraction of Local Features to Improve the Accuracy of Handwriting Signature Recognition. Journal of Machine Vision and Image Processing, 2015; 2(1): 33-43.
)