A Comparative Study of Machine Learning Approaches for Handwriter Identification

Durou, Amal, Al-Maadeed, Somaya, Aref, Ibrahim, Bouridane, Ahmed and Elbendak, Mosa (2019) A Comparative Study of Machine Learning Approaches for Handwriter Identification. In: 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3). IEEE, pp. 206-212. ISBN 978-1-5386-7002-6

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Abstract

During 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.

Item Type:	Book Section
Uncontrolled Keywords:	convolutional neural network, writer identification, feature extraction, machine learning
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	05 Jun 2019 14:29
Last Modified:	10 Oct 2019 18:31
URI:	http://nrl.northumbria.ac.uk/id/eprint/39508

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