On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Tight approximation algorithms for maximum general assignment problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
Off-line Handwritten Signature GPDS-960 Corpus
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Pattern Recognition
ICDAR 2009 Signature Verification Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Classification approaches in off-line handwritten signature verification
WSEAS Transactions on Mathematics
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Automatic Signature Verification: The State of the Art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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The computerized verification of scanned, handwritten signatures has been extensively studied in the past decades, but there are still several possibilities for improvement in the field. To achieve better verification results, we propose a simplified probabilistic model for off-line signature verification. In our model, each of the verification steps can be mathematically described and, therefore, individually analyzed and improved. Using this model, we are able to predict the accuracy of a signature verification system based on just a few a priori known parameters, such as the cardinality and the quality of input samples. Several experiments have been conducted using our statistics-based classifier to confirm the assumptions and the results of our model. Based on the results, we can provide answers to several old questions within the field, such as why is it so hard to achieve error rates below 10% or how does the number of original samples and features affect the final error rates.