Dynamic programming algorithm optimization for spoken word recognition
Readings in speech recognition
On-line signature verification based on split-and-merge matching mechanism
Pattern Recognition Letters
Off-Line Signature Verification by Local Granulometric Size Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signature identification through the use of deformable structures
Signal Processing - Special issue on deformable models and techniques for image and signal processing
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Local and Global Feature Selection for On-line Signature Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Off-line Chinese signature verification based on support vector machines
Pattern Recognition Letters
On-line signature recognition based on VQ-DTW
Pattern Recognition
Computer Detection of Freehand Forgeries
IEEE Transactions on Computers
Automatic signature verification based on accelerometry
IBM Journal of Research and Development
Improved DTW algorithm for online signature verification based on writing forces
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Signature verification (SV) toolbox: Application of PSO-NN
Engineering Applications of Artificial Intelligence
Boundary-based lower-bound functions for dynamic time warping and their indexing
Information Sciences: an International Journal
Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry
Information Sciences: an International Journal
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In this paper, a method for the automatic handwritten signature verification (AHSV) is described. The method relies on global features that summarize different aspects of signature shape and dynamics of signature production. For designing the algorithm, we have tried to detect the signature without paying any attention to the thickness and size of it. The results have shown that the correctness of our algorithm detecting the signature is more acceptable. In this method, first the signature is pre-processed and the noise of sample signature is removed. Then, the signature is analyzed and specification of it is extracted and saved in a string for the comparison. At the end, using adapted version of the dynamic time warping algorithm, signature is classified as an original or a forgery one.