An Algorithm for Classifying Handwritten Signatures Using Convolutional Networks

In this study, a model based on convolutional neural networks is proposed to quickly and efficiently classify and identify a person's signature with over 90% accuracy. For this purpose, two signature datasets were used. The first, called CEDAR, is publicly available. The second set, called GC-D...

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Bibliographic Details
Published in:Revista IEEE América Latina 2022-03, Vol.20 (3), p.465-473
Main Authors: Culqui-Culqui, German, Sanchez-Gordon, Sandra, Hernandez-Alvarez, Myriam
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Classification algorithms
Convolutional neural networks
Datasets
Handwriting
Handwritten signature classification
IEEE transactions
Offline signature recognition
Portable equipment
Signatures
Stacking
Support vector machines
Three-dimensional displays
Transforms
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Summary:In this study, a model based on convolutional neural networks is proposed to quickly and efficiently classify and identify a person's signature with over 90% accuracy. For this purpose, two signature datasets were used. The first, called CEDAR, is publicly available. The second set, called GC-DB, was collected by the researchers using uncontrolled environments (different signing positions). This dataset has 121 local signatories from the Republic of Ecuador, who submitted 45 copies of signatures each. In this set of signatures, the implicit noise produced by the capture device and by the paper of different thicknesses used in the collection made noise removal a relatively complex operation. The effectiveness of the proposed algorithm was compared with two other algorithms that were implemented and validated using the two data sets. The results show that it is possible to perform an efficient classification of handwritten signatures with the developed algorithm. Additionally, the developed algorithm is lightweight and easy to implement and can be installed on portable devices such as cell phones or tablets.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2022.9667145