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AuthorDurou A.
AuthorAl-Maadeed S.
AuthorAref I.
AuthorBouridane A.
AuthorElbendak M.
Available date2020-04-09T12:27:28Z
Publication Date2019
Publication NameProceedings of 12th International Conference on Global Security, Safety and Sustainability, ICGS3 2019
URIhttp://dx.doi.org/10.1109/ICGS3.2019.8688032
URIhttp://hdl.handle.net/10576/13990
AbstractDuring the past few years, writer identification has attracted significant interest due to its real-life applications including document analysis, forensics etc. Machine learning algorithms have played an important role in the development of writer identification systems demonstrating very effective performance results. Recently, the emergence of deep learning has led to various system in computer vision and pattern recognition applications. Therefore, this work aims to assess and compare the performance between one of the deep learning algorithms, AlexNet model, with two of the most effective machine learning classification approaches: Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN). The evaluation has been conducted using both IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting.
SponsorThis work is supported by the Qatar National Research Fund through National Priority Research Program (NPRP) No 7-442-1-082. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectconvolutional neural network
Subjectfeature extraction
Subjectmachine learning
Subjectwriter identification
TitleA Comparative Study of Machine Learning Approaches for Handwriter Identification
TypeConference Paper


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