Robot vision
Character image enhancement by selective region-growing
Pattern Recognition Letters
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
Off-line Chinese signature verification based on support vector machines
Pattern Recognition Letters
A New Off-line Signature Verification Method based on Graph
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Off-line signature verification systems: a survey
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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A Hidden Markov Model (HMM) approach to off-line signature verification is presented. First, each of the signature images is represented as a landmark point set, which includes turning points, isolated points, trifurcate points, intersection points and termination points on signature skeleton. Then we propose a novel deformable grid partition technique. Based on landmark point matching, we build the matching relations between planar regions to get the deformable grids, and then extract grid features from them. By using HMM in signature modeling, the deformable grid partition method shows remarkable improvements over traditional grid partition methods in discriminative ability.