Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43949
Title: Writer identification approach by holistic graphometric features using off-line handwritten words
Authors: Vásquez, José L.
Dutta, Malay Kishore
Travieso González, Carlos Manuel 
Ravelo García, Antonio Gabriel 
Alonso Hernández, Jesús Bernardino 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Based-handwriting recognition Word holistic analysis Graphometric features Off-line system Biometric identification
Issue Date: 2018
Publisher: 0941-0643
Journal: Neural Computing and Applications 
Abstract: The biometric identification is an important topic with applications in different fields. Among the different modalities, based-handwriting biometric is a very useful and extended modality, and the most known one is the signature. The use of handwritten texts is researched presenting a biometric system for identifying writers from their handwritten words. A set of feature-based graphometric information has been extracted from off-line handwritten words to implement an automatic biometric approach. Given the handwritten nature of the information and its great variability, a feature selection based on principal component analysis and neural network classifier has been proposed. A fusion block based on neural networks has been added in order to reduce the effect of the data variability due to an increase and stabilization of the accuracy. A dataset composed of 100 writers have been used for the experiments. A holdout cross-validation was applied and the accuracy reached between 99.80% and 100%
URI: http://hdl.handle.net/10553/43949
ISSN: 0941-0643
DOI: 10.1007/s00521-018-3461-x
Source: Neural Computing and Applications[ISSN 0941-0643], p. 1-14
Neural Computing and Applications [0941-0643], n. 32, p. 15733–15746
Appears in Collections:Artículo preliminar
Unknown (1,4 MB)
Show full item record

SCOPUSTM   
Citations

1
checked on Feb 21, 2021

Page view(s)

43
checked on Feb 21, 2021

Download(s)

1
checked on Feb 21, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.