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This work proposes an off-line writer identification approach based on graphometrical and forensic features. We selected a set of features with independence of the text and some stability degree to natural changes in the writing. The system uses the LS-SVM classifier with RBF kernel, reaching up to 99.1% of success rate for an own database composed by 100 users with 10 samples per each one.
In this work is proposed a writer identification approach based on graphometrical and forensic features. The proposal replies to an off-line system, where the handwriting is provided before to perform the analysis. An Artificial Neural Network is used as classifier and after the decision fusion module, the system reaches up to 94.6% of success rate for a own database composed by 100 users with 10...
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