On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individuality of Handwriting: A Validation Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Learning Strategies and Classification Methods for Off-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
On the Power of Feature Analyzer for Signature Verification
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
Offline signature verification using the discrete radon transform and a hidden Markov model
EURASIP Journal on Applied Signal Processing
Time-efficient stroke extraction method for handwritten signatures
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
The curling vector field transform of gray-scale images: a magneto-static inspired approach
WSEAS Transactions on Computers
WSEAS Transactions on Information Science and Applications
A general approach to off-line signature verification
WSEAS Transactions on Computers
Analysis of Authentic Signatures and Forgeries
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Classification approaches in off-line handwritten signature verification
WSEAS Transactions on Mathematics
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One of the major challenges in off-line signature verification is the fact that a person's own signature is influenced by a number of external and internal factors. This influence results in a high variability even between signatures written by the same signer. This paper proposes a method which is able to model the intra-person variability of a signature feature and also to identify and eliminate the effects of external factors. To demonstrate the efficiency of the algorithm, a sample signature verifier is constructed and evaluated on the Signature Verification Competition 2004 database. Experiments have shown that by using 3 features (endings, loops and skew vectors) an average error rate of 12% can be achieved by the system. These results may be further improved by increasing the number of features, used during the comparison of signatures.