Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/3172
Title: Signature verification using wavelet, gabor, convolution neural network (CNN)
Authors: Abu Jamra, Samir
Advisors: Dagher, Issam 
Subjects: Neural networks (Computer science)
Artificial intelligence
Intelligent agents (Computer software)
Issue Date: 2017
Abstract: 
Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. However, Signature Verification Systems experience the ill effects of factors that influence the execution, for example, composing instrument, paper, and physical state of the author. On the other head, for this system to be accurate, a considerably large number of samples is required. This report focuses on offline signature verification to determine whether the signature is genuine or forgery. In our research we use Convolution Neural Network (CNN) to classify handwritten digits in two types of datasets: the MNIST database, and UTSIG database. To obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter comparing outputs of both CNN and classify one results which is 99% accuracy.
Description: 
Includes bibliographical references (p. 31-33).

Supervised by Dr. Issam Dagher.
URI: https://scholarhub.balamand.edu.lb/handle/uob/3172
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Ezproxy URL: Link to full text
Type: Project
Appears in Collections:UOB Theses and Projects

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