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- W2758228359 abstract "This work present new parameters based on biometrie handwritten information for the writer identification. The feature extraction is developed by new algorithms based on image processing techniques. The handwritten parameters will be classified by artificial neural network and fusion strategy in order to increase the accuracy. After experiments, and using a dataset composed by 100 writers, this proposal reaches an accuracy of 82.7%." @default.
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- W2758228359 date "2017-02-01" @default.
- W2758228359 modified "2023-09-30" @default.
- W2758228359 title "Applying forensic features on writer identification" @default.
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- W2758228359 doi "https://doi.org/10.1109/spin.2017.8050015" @default.
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