A Comparative Study of Machine Learning Approaches for Handwriter Identification

Amal Durou, Somaya Al-Maadeed, Ibrahim Aref, Ahmed Bouridane, Mosa Elbendak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of 12th International Conference on Global Security, Safety and Sustainability, ICGS3 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages206-212
ISBN (Electronic)9781538670019
ISBN (Print)9781538670026
DOIs
Publication statusPublished - 11 Apr 2019
Event12th International Conference on Global Security, Safety and Sustainability, ICGS3 2019 - London, United Kingdom
Duration: 16 Jan 201918 Jan 2019

Conference

Conference12th International Conference on Global Security, Safety and Sustainability, ICGS3 2019
CountryUnited Kingdom
CityLondon
Period16/01/1918/01/19

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