On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Segmenting Handwritten Signatures at Their Perceptually Important Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Delta LogNormal theory for the generation and modeling of cursive characters
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Why Handwriting Segmentation Can Be Misleading?
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
ER2: An Intuitive Similarity Measure for On-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectrum Analysis Based onWindows with Variable Widths for Online Signature Verification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
On-Line Signature Verification by Exploiting Inter-Feature Dependencies
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Off-line Signature Verification based on the Modified Direction Feature
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Off-Line Signature Recognition and Verification by Kernel Principal Component Self-Regression
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Techniques for static handwriting trajectory recovery: a survey
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
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Most of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The window's positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes.